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Valuing Utility Offsets to Regulations Affecting
Addictive or Habitual Goods
August 3, 2015
Office of the Assistant Secretary for Policy and Evaluation
U.S. Department of Health and Human Services
2
Acknowledgements
This paper was prepared for the Office of the Assistant Secretary for Planning and Evaluation (ASPE),
U.S. Department of Health and Human Services (HHS), by David Cutler (Harvard University), Amber
Jessup (ASPE/HHS), Donald Kenkel (Cornell University), and Martha Starr (U.S. Food and Drug
Administration and American University). The contributions of Dr. Cutler and Dr. Kenkel were funded
through subcontracts with Mathematica Policy Research. Amber Jessup is the HHS Project Manager.
This paper was informed by reviews of the research literature, discussions with prominent
experts on health and behavioral economics, and original research. Valuable feedback on earlier
versions of this work came from Frank Chaloupka (University of Illinois at Chicago), James Choi (Yale
University), Sherry Glied (New York University), James K. Hammitt (Harvard University), Joseph
Newhouse (Harvard University), and Kenneth Warner (University of Michigan), as well as a number of
economists from HHS, FDA, Office of Management of the Budget, the Council of Economic Advisors and
researchers from the Centers for Disease Control and Prevention. We are also grateful for the
contributions of the numerous HHS and FDA staff who supported this effort, and to Lisa Robinson for
valuable comments and advice.
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Table of Contents
Acknowledgements.................................................................................................................... 2
Table of Contents ...................................................................................................................... 3
Executive Summary.................................................................................................................... 4
1.0 Introduction......................................................................................................................... 5
2.0 Regulatory Context ............................................................................................................... 7
2.1 Defining Addictive and Habitual Goods................................................................................10
2.2 Previous Analyses of Addictive and Habitual Goods ...............................................................11
3.0 Economic Framework ...........................................................................................................12
3.1 Defining Rational Behavior ................................................................................................13
3.2 Conceptual Framework .....................................................................................................15
3.3 Modeling Regulations .......................................................................................................18
3.3.1 Regulations that Affect Information ..............................................................................19
3.3.2 Withdrawal versus Sustained Utility Losses ....................................................................21
3.3.3 Regulations that Affect Price or Product Attributes .........................................................23
3.4 Summary ........................................................................................................................24
4.0 An Empirical Approach to Welfare Evaluation ..........................................................................25
4.1 Impact of Regulations on Cessation ....................................................................................26
4.2 Valuing Effects on Initiation ...............................................................................................34
4.3 Population Estimates ........................................................................................................36
5.0 Conclusions ........................................................................................................................43
References...............................................................................................................................45
Appendix 1: Alternative Approaches ............................................................................................53
A.1 Measuring Willingness to Pay ............................................................................................53
A.2 Calculating Compensating Differentials ...............................................................................55
A.3 Directly Measuring Effects of Smoking Cessation on Subjective Well Being ...............................56
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Executive Summary
In analyzing the value consumers place on goods, economic analysis begins with the
presumption that consumers reveal their preferences through their consumption behavior. This view is
based on the notion that consumers are well informed and well situated to rationally balance the costs
and benefits of purchasing a specific good or service. Under these assumptions regulations that reduce
consumption of a good or service necessarily result in utility or welfare loss.
There are several reasons to believe that consumption of addictive and habitual goods does not
conform to the simple economics of consumer choice. First, consumers may not be fully informed about
all costs, benefits and risks associated with such products. Information may be costly to acquire,
complex and difficult to interpret. Or it may be deliberately distorted by companies aiming to promote
their products. A second set of factors relates to the nature of the addictive and habitually-consumed
products and ability to make rational choices. For example in the case of cigarettes young people may
systematically misjudge or deny their likelihood of becoming addicted. Regular smokers may misjudge
their ability to quit or underweight consequences of their consumption that occur in the distant future.
Addiction and habituation will tend to make such errors in choice cumulate over time.
In analyzing economic impacts of regulations aimed at addictive and habitual goods, a particular
challenge concerns the issue of lost utility, sometimes referred to as lost consumer surplus. Regulations
that induce smokers to quit or that dissuade people from eating foods that are high in calories, fats,
sodium, or sugars have health benefits that can be quantified using standard methods of cost-benefit
analysis. But should cost-benefit analysis also account for lost satisfactions of consumption people
experience when they reduce their intake of such goods? If so on what basis could we value such “utility
offsets” to health benefits of regulations?
This paper develops a method for analyzing utility offsets to health benefits of regulations
affecting addictive or habitual goods, with special emphasis on the case of smoking. The approach is
analytically consistent with up-to-date representations of consumer behavior. It can also be
implemented using existing data. The approach rests on identifying consumers most likely to be well-
informed and to choose their consumption levels in ways that rationally weigh costs, benefits and risks.
Then the values these consumers place on given levels of consumption can be used to estimate losses in
utility that may result from regulations. Two different methods of identifying such consumers are used.
The first takes smokers who do not meet criteria for nicotine dependence as the rational benchmark, on
the grounds their consumption levels are less likely to be biased up by addiction. The second singles out
smokers between the ages of 30 and 45 who have 4-year college degrees, on the grounds that they
began smoking in an era when its health risks were well-known and that their education provides them
with skills and knowledge relevant to accurately assessing benefits, costs and risks of smoking.
Using this framework, we show that utility changes caused by regulations depend in part on the
type of regulation, as regulations that primarily increase information work somewhat differently than
regulations that raise the cost of smoking. Utility changes also differ across categories of consumers.
Existing smokers induced to quit by a regulation experience three potential utility effects: short-term
withdrawal costs, utility loss from reduced smoking satisfactions, and money savings. The first two items
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are subtractions from health benefits; the third adds to them. The analysis is somewhat different for
reduced initiation. Potential smokers dissuaded from starting by the regulation realize money savings,
but they do not incur withdrawal costs or utility losses, since they never initiate. Finally, existing smokers
who continue to smoke or potential smokers who continue to initiate are not affected by regulations
that increase information without changing their smoking behavior. But regulations that increase the
cost of smoking do reduce their utility because higher costs imply reduced consumption in general.
Our approach differs from two recent lines of work in this area. Unlike Ashley et al. (2015), we
specifically analyze how regulations affect smoking initiation, showing that dissuading people from
starting to smoke entails no utility offset. Unlike Chaloupka et al (2015), we recognize that existing
smokers may experience utility losses even if they wish they had never started to smoke, because the
preferences via which they evaluate utility changes reflect their consumption experience.
We use this approach to work through examples of potential regulations, illustrating how
estimated utility offsets can be calculated. The empirical analysis shows that the utility cost of smoking
cessation is small relative to the health gains in people for whom withdrawal costs are the main utility
loss of quitting, and even among people who have some ongoing loss, the utility offsets represent 20-25
percent of the health gains. While marginal smokers induced to quit by regulations can be expected to
have low or no steady-state loss, even this higher estimate is far below prevailing estimates of the utility
cost of smoking used by FDA and other analysts. The net result is that there are clear offsets to health
benefits from reduced consumer utility associated with reduced smoking, but they are far smaller than
some recent estimates in the literature.
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1.0 Introduction
Consumption of many addictive and habitual goods has significant health costs. In the U.S., smoking
accounts for 480,000 premature deaths annually (CDC 2014a). Excess alcohol consumption resulted in
approximately 88,000 premature deaths annually from 2006 to 2010 (CDC 2014b). And over 110,000
deaths were linked to obesity in 2000 (Flegal 2005). In this context, would regulations aimed at curbing
smoking, drinking, or excess caloric intake be welfare enhancing? There can be substantial health
benefits from policies that bring consumption of addictive and habitual goods down to levels consistent
with maintaining good health. Yet consumers may also lose some satisfactions from their consumption if
they cut back on goods they especially like. This makes it important to examine the extent to which
expected health benefits from proposed regulations are offset by lost utility from consumption.
This paper investigates the question of whether there are utility offsets to health benefits of
policies addressing consumption of addictive and habitual goods, and if so, how their monetary value
can be estimated. We use smoking as an example to illustrate approaches that can be implemented
given the data now available, and to identify the areas where more data and research are needed.
Typically, economists assume that consumers reveal their preferences through their actions. If
people are well informed about the health risks of a good they consume, to themselves and people
around them, and if they fully factor these costs into their consumption decisions, they will incur these
health risks only if the ancillary benefits they receive exceed the health loss.1 In this situation, a
regulation that restricts access to a good would necessarily lead to a welfare loss.
However, with addictive and habitually consumed goods, consumption in the absence of
regulation may not accurately reflect individual preferences and full and accurately-processed health
information. This may occur because the consumer has only partial information or the available
information may be inadequate or difficult to interpret, due to intentional distortions by industry or
other factors. For example, advertisements that make smoking appear sophisticated, mature, or “cool”
might induce people to smoke without fully considering the long-term health costs. It also may occur if
consumers find it difficult to act in accordance with their preferences, instead acting impulsively and
finding it challenging to exercise self-control, or reacting emotionally without carefully considering the
consequences of their behavior. Examples abound – we make numerous decisions in the face of limited
information, and often have problems eating less, exercising more, and engaging in other activities that
are likely to provide significant benefits in the long run, even as we express a desire to do so.
In some cases, the product itself has attributes that make it difficult to consume in a rational
way. This is a particular concern in the case of addictive or habitual goods. Even when people know
about the long-term health costs of a product, they may believe that they will not become addicted, that
the health harms will happen only to others, or that quitting will be easy when they are eventually
motivated to do so. In such cases, regulation may aid individuals in becoming better informed and acting
more in accordance with their own preferences.
1 Externalities are a major issue with many addictive and habitual goods, including tobacco use (Cutler 2002: 2156-62). Because this paper’s concern is with the issue of utility offsets, for simplicity it does not address the question of external effects of consumption on others, which would lead to welfare losses even if individuals make fully informed decisions.
7
For addictive or habitual goods, these mistaken understandings and difficulties in decision-
making may compound over time. A person may try cigarettes without realizing the long-term health
costs of smoking, and only realize those health costs after they have become addicted. The result can be
large and growing differences between consumers’ true preferences and their behavior. In this set of
circumstances, the utility loss consumers experience from reducing their consumption of the addictive
good and replacing it with consumption of other goods may be very small, because the value of the
utility they were receiving from consumption of the addictive good was not high to begin with. In this
case, regulations aiming to reduce overconsumption of goods with adverse health consequences will be
clearly welfare improving, as the health benefits will be large relative to any utility loss.
In this paper, we develop a framework for analyzing the utility consequences of regulations
addressing addictive and habitual goods and apply this analysis to the empirical example of smoking
regulations. Our preferred approach values the benefits of regulations based on data on individuals who
are likely to be well-informed and behaving consistently with their own preferences, on the grounds that
their valuation of consumption provides a good approximation to the utility it yields. We also consider
ways in which this approach could be improved by further data collection and analysis, and identify
other approaches that may be worthy of further development.
Before we present our analysis, we delineate some boundaries. First, our concern is with
estimating benefits of regulations to individuals who consume the good or may potentially start to
consume the good. Full benefit-cost analyses also consider the external consequences of consumption
for non-consumers, such as second hand smoke or insurance costs associated with caring for sicker
individuals, as well as costs to industry and government. These are important components of welfare
analysis, but not the focus of our paper. Second, we are not centrally concerned with estimating the
health gains to individuals of reduced consumption of addictive and habitual goods. Analysis of expected
health benefits is best done in the context of specific regulations being considered. Rather our concern
is with estimating possible utility offsets to those gains.
We begin in Section 2 with the regulatory context for government intervention. Section 3 lays
out the economic framework for welfare changes of regulations affecting addictive or habitual goods.
Then, in Section 4, we describe our approach for measuring potential utility offsets to health benefits of
proposed regulations. Section 5 concludes and lays out a pathway for implementation. An appendix
discusses alternative models that might be used for evaluation of addictive and habitual goods in the
future.
2.0 Regulatory Context
HHS and its agencies are authorized to promulgate regulations under numerous statutes. Two are
particularly relevant for the regulation of addictive and habitual goods: the Federal Food, Drug and
Cosmetic Act (FD&CA) and the Family Smoking Prevention and Tobacco Act (TCA). The FD&CA gives the
U.S. Food and Drug Administration (FDA) regulatory authority over most drugs, biological products,
medical devices, and cosmetics and certain foods. The TCA gives FDA regulatory authority over the
manufacture, distribution, and marketing of tobacco products to protect public health. As part of the
federal rulemaking process, HHS and its agencies are required by Executive Orders 12866 and 13563 to
analyze the benefits and costs of major proposed and final regulations. When implementing these
8
Executive Orders, HHS and other agencies must follow analytic requirements developed by the U.S.
Office of Management and Budget (OMB 2003).
Multiple impacts enter into the analysis of benefits and costs (Table 1). The benefits are the
impacts on consumers, which in principle may be positive (like better health) or negative (like lost
utility). Negative impacts may be treated as negative benefits or positive costs; it is a matter of
convention which method is chosen, as the bottom-line calculation of net benefits to consumers will be
the same either way. The current paper treats negative effects on consumers as negative entries on the
benefit side.
Traditionally, total net benefits of a regulation are measured by consumers’ willingness to pay
for its provisions, which measures what they are willing to give up (in terms of consumption of other
goods and services) to gain the benefits of the regulation. For many HHS regulations, a major
component of willingness to pay is the health improvements the population can be expected to
experience. Health improvements include both increases in life expectancy and reductions in illnesses
and chronic conditions that would flow from reducing consumption of the harmful product.
Improvements may be experienced by both individuals who have been consuming the product and by
others whose health has been adversely affected by being exposed to their consumption (e.g. via
secondhand smoke). As noted above, we do not analyze external consequences of improved health
associated with reduced use of addictive or habitual goods, and rather focus on individuals who
consume the good or are deterred from consuming it.
Consumption decisions are not driven solely by health considerations. Other factors likely to
affect willingness to pay include the utility people receive from consuming the product. For cigarettes,
these may potentially include relaxation, increased attention, projecting a certain identity, or enjoying
the experience of smoking. For foods that are high in calories, fats, or sugar, they may include taste,
convenience, or brand loyalty. However, because curbing consumption of a good due to a regulation
frees up money for spending on other goods, utility that consumers lose will be offset to some degree
by utility gains from increased consumption of other products. The difference between the two is
concept of consumer surplus.
The costs of regulations are typically measured as the compliance costs to industry and the
monitoring and enforcement costs for government agencies. We do not estimate these costs in this
paper. Rather, we focus on the question of how to value lost consumer surplus from policies addressing
addictive and habitually consumed goods – the bold parts of Table 1.2
2 HHS and FDA do not include improved productivity among the benefits of regulations that improve health, because doing so would double-count the benefits of better health status.
9
Table 1: Examples of Benefits and Costs of Regulations
Benefits Costs
Impacts on consumers: Companies’ costs of compliance:
- Longer life expectancy - Product approvals and submissions
- Better health status - Product modifications - Reduced externalities (e.g. second-hand smoke) - Redesigning labels or packaging
- Lower medical expenditures - Product testing
- Loss in utility from forgoing consumption of the addictive good
Monitoring and enforcement costs to government
- Gain in utility from increased consumption of other goods
While methods for estimating health benefits are relatively well-established, methods for
estimating potential loss in consumer surplus are not. Ordinarily, economists assume that people will
take the various attributes of products, including both health and non-health considerations, into
account when they make consumption decisions. Health costs are like a non-monetary price paid along
with the monetary price of buying the good. Thus, well-informed consumers should consider them the
same way, and analysis should be able to use information on market demand for the product to infer
the value they place on the good. However, information gaps or decision-making difficulties may break
this link, especially for addictive or habitual goods. People may try addictive or habitual goods before
they are fully informed about the addictiveness of the product or its health costs, or they may find the
short-term withdrawal costs of ceasing consumption overwhelming even if the long-term health
benefits are significant. These issues pose notable complications for estimating willingness to pay for
addictive and habitually-consumed goods.
Several features suggest that current and potential smokers often do not fully incorporate or
accurately value the negative health costs of consuming tobacco products. The first is the importance of
decisions made when young. In the case of cigarette smoking, 87 percent of first use of cigarettes occurs
by age 18, with nearly all first occurring use by age 26 (98 percent) (CDC 2014a). Research finds that
youths are likely to overestimate benefits of risky activities and undervalue their risks more than is true
for adults (Reyna and Farley 2006). They may also discount future consequences of their current actions
by more than adults (Chabris et al. 2008). Indeed, research establishes the prefrontal cortex of the brain,
which is responsible for cognitive analysis and abstract thought, does not fully mature until
approximately age 25 (Gogtay et al. 2004; Galvan et al. 2006). Thus, there is an inherent suspicion of the
rationality of youth decision making.
A good example of young people underestimating the health risks they run from smoking
concerns their predictions of their future smoking. In a study of high school students in the 1996-2007
senior classes, 55 percent of students who were smoking daily said they would not be smoking five years
later, but only 24 percent of them had in fact quit five years hence (CDC 2012: 249).
Adult smokers too seem to overestimate the ease of quitting. The 2010-11 Tobacco Use
Supplement to the Current Population Survey asked current adult smokers, “If you did try to quit
smoking altogether in the next 6 months, how LIKELY do you think you would be to succeed – not at all,
a little likely, somewhat likely or very likely?” About 13 percent of everyday smokers chose “not at all”;
25 percent “a little likely”; 39 percent “somewhat likely”; and 24 percent “very likely.” In contrast, only
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5-10 percent of cessation attempts are successful. Thus, even the “very likely” share alone
overestimates quitting success (24 percent v. 5-10 percent), and the “very likely” and “somewhat likely”
shares vastly overestimate it (63 percent vs. 5-10 percent).3
Taken as a whole, the analysis shows that regulatory analyses need to directly address the
degree to which regulatory-induced changes in consumption of addictive or habitual goods add to or
subtract from welfare, rather than simply make assumptions based on economic theory.
2.1 Defining Addictive and Habitual Goods
According to the National Institute for Drug Abuse, addiction is a “chronic, relapsing brain disease that is
characterized by compulsive drug seeking and use, despite harmful consequences. It is considered a
brain disease because drugs change the brain; they change its structure and how it works. These brain
changes can be long lasting and can lead to many harmful, often self-destructive, behaviors.”4
The concept of “habit” is less clear cut. Scientific and medical researchers tend to understand
“habit” as a behavior engaged in automatically, without conscious thought. While habits generally
involve compulsive behavior and may be rewarding or reinforcing, unlike addiction they do not typically
involve tolerance, psychoactive effects, or withdrawal. Economically, addiction and habit are typically
modeled as a situation where the marginal utility (or sense of satisfaction or well-being) associated with
current consumption of a good depends on the stock of past consumption.
There are multiple validated measures of nicotine dependence, including the Fagerström Test
for Nicotine Dependence (FTND),5 the Wisconsin Inventory of Smoking Dependence Motives, the
Nicotine Dependence Syndrome Scale, and the American Psychiatric Association’s Diagnostic and
Statistical Manual (DSM). Analyses using all of these measures suggest that a substantial share of
smokers is nicotine dependent. The Substance Abuse and Mental Health Services Administration
(SAMHSA) estimates that 57 percent of past 30 day smokers in the National Survey on Drug Use and
Health are nicotine dependent (SAMHSA 2013).6 Further, survey data show that the majority of smokers
want to quit but find it very difficult to do so. In 2010, 69 percent of adult smokers in the National
Health Interview Survey said they were interested in quitting smoking completely, and 52 percent had a
past-year quit attempt (CDC 2011).7 Yet only 6 percent had recently succeeded in quitting.8 Two-thirds
of quit attempts were undertaken without smoking-cessation aids. Rates of successful quitting vary
strongly with education: they were highest (11 percent) among smokers with an undergraduate degree
3 Authors’ computations from the CPS-TUS data using sample weights provided by the U.S. Census Bureau. 4 http://www.drugabuse.gov/publications/media-guide/science-drug-abuse-addiction-basics. 5 The Fagerström Test measures tobacco dependence using multiple factors like time from awakening to the first cigarette, number of cigarettes smoked, and compulsion to use, but most of its ability to predict ability to quit is from the time to the first cigarette after waking measure (Baker et al. 2007). 6 This estimate considers a smoker to be nicotine dependent if the first cigarette is smoked within 30 minutes of waking (a key measure from the FTND) or the person scores 2.75 or more on the 19-item Nicotine Dependence Syndrome Scale (SAMHSA 2010). 7 This rate is based on former smokers who had stopped smoking in the past year and current smokers who had refrained from smoking for one or more days in the past year because they wanted to quit. 8 Quitting is defined here as not having smoked at all in the 6 months preceding the interview.
11
and lowest (3.2 percent) among smokers with less than 12 years of education. This finding contributes
to our definition of rational smoking below.
Although successful quitting is difficult, quitting attempts are frequent enough that a large share
of ever smokers have quit. The share of U.S. adults who are former smokers now exceeds the share who
currently smoke (21 vs. 18 percent; Blackwell et al. 2014). Among people in a given birth cohort, the
prevalence of smoking declines with age, so by age 65 the share still smoking is substantially below its
peak (Anderson et al. 2012). Annual probabilities of smoking cessation at any given age are also trending
up (CDC 2014a: 729).
2.2 Previous Analyses of Addictive and Habitual Goods
Previous analyses illustrate the difficulty of estimating the changes in consumer surplus associated with
regulations of addictive or habitual goods. For example, in 2004, FDA declared dietary supplements
containing ephedrine alkaloids “adulterated” because they imposed unreasonable risks, effectively
removing those products from the market (FDA 2004). FDA noted that some consumers would
experience a loss in utility because the regulation forces them to move from their preferred (ephedrine-
containing) product to a substitute (e.g., for weight loss or improved athletic performance). Because
willingness to pay estimates were not available, FDA instead tested the sensitivity of its results to
different assumptions about the premium consumers might be willing to pay for ephedrine-containing
products, testing the impact of estimates ranging from 1 percent to 10 percent of the product’s price.
Another example is FDA’s 2014 proposed regulation revising the nutrition facts panel required
on certain food items (FDA 2014a). The supporting analysis considered that, in addition to market
failures (such as information gaps) consumers may find it difficult to behave consistently with their
preferences, due to the difficulty of self-control and other problems. In such cases, the labeling changes
could help consumers by making the nutritional properties of foods more salient at the point of
purchase or consumption.
New regulatory authorities under the TCA pose the most significant and relevant challenges for
HHS and FDA in measuring consumer surplus changes. Recent rulemaking efforts that addressed these
issues include FDA’s analysis of the graphic warnings label rule for cigarettes (FDA 2011)9 and its 2014
proposed rule deeming various types of tobacco products to be subject to the TCA (FDA 2014b). Both
rules rely conceptually on the framework set forth by the Australian Productivity Commission (2010) and
Weimer et al. (2009). The Australian Productivity Commission addresses the benefits of gambling
policies by distinguishing between “recreational” and “problem” gamblers and inferring the welfare
benefits from the preferences of recreational gamblers. Weimer et al. apply a similar framework to
cigarette smokers, estimating the benefits of tobacco regulations using a contingent valuation survey to
elicit smokers’ preferences.
The analysis of Ashley et al. (2015) is most relevant to this paper. Ashley et al. adjust the
discounted value of the health benefits of regulation downwards to account for estimated loss of
consumer surplus, using parameter values based on simulations in Gruber and Köszegi (2001). They
9 The graphic warning labeling final rule was not implemented as a result of court rulings that its requirements violated the first amendment.
12
estimate that between 10 and 90 percent of the value of the health gains of reduced smoking is offset
by the consumer surplus loss.
These analyses have generated criticism. In particular, Chaloupka et al. (2015) critique the
estimates of consumer surplus in the FDA’s (2011) analysis of graphic warning labels, asserting that
analysts should not subtract positive aspects of smoking from health benefits of quitting for smokers
who become regular users as minors, because minors have imperfect information and insufficient
understanding of the addictiveness of nicotine. For these smokers, Chaloupka et al. suggest that the
analysis should consider only the health benefits that accrue to consumers from quitting smoking. For
smokers who initiate smoking as an adult, they conclude that there is too much uncertainty to quantify
the consumer surplus losses associated with quitting, and the analysis should not include quantitative
estimates of these effects.
In the following section, we aim to improve the analytical foundations of approaches used to
analyze potential losses in consumer surplus when estimating benefits to consumers of these types of
regulations. Our analysis departs in several ways from both FDA’s earlier analyses and the critiques of
those analyses. These differences include an evaluation of changes in initiation as well as cessation, and
direct consideration of the short-term withdrawal costs associated with cessation, relative to possible
ongoing utility losses associated with continuing to smoke.
3.0 Economic Framework
Consumers may experience a number of changes in utility, both positive and negative, when they
reduce their consumption of addictive or habitual goods. Table 2 presents some of the effects people
experience when they reduce their consumption due to a regulation. People who are affected include
both existing users who cut back or quit consuming the good, and people who are deterred from
starting to consume it due to the regulation. On the negative side, people who reduce consumption of
habitual goods face costs of changing their habits; for example, those who reduce their consumption of
foods that are high in calories, sugars, or fat face costs of finding pleasurable alternatives. People who
stop consuming addictive goods also bear withdrawal costs. For smokers, withdrawal symptoms include
irritability, headaches, anxiety, insomnia, and difficulty concentrating (Hughes 2007); discomforts of
withdrawal can be severe and explain the fact that most quit attempts fail within a week. Consumers
also lose the satisfactions they received from consumption. In the case of smoking, this may include
relaxation, enhanced attention, or weight control. In the case of foods, satisfactions may be pleasing
taste, appetizing smell, or appealing appearance. If people value these attributes and are not readily
able to find equally satisfying substitutes, then estimates of the effects of the regulation should include
this loss. At the same time, consumers may experience gains in utility when they reduce their
consumption of addictive or habitual goods. For example, smokers who kick the habit may lose feelings
of self-loathing or embarrassment from being a smoker, and improve their sense of self-efficacy from
having undertaken a difficult transition. Estimating the effects of regulations on consumers should try to
value both negative and positive effects on utility.
Many of the benefits and costs of regulation are difficult to put dollar values on, since they are
experienced in utility terms. Is a former smoker who quit because of a regulation that made smoking
13
more costly better off than they would have been in the absence of regulation? Monetizing the net
benefit is difficult and depends on how rationally smoking and cessation decisions are made.
Table 2: Examples of Positive and Negative Utility Effects on Individuals who
Reduce their Consumption due to a Regulation
Negative Positive
For existing users: Costs of withdrawal or changing habits
Increased sense of self-efficacy, reduced self-loathing
Loss of positive attributes of consumption
Money freed up for spending on other goods
Loss of positive identity projection (being cool or defiant)
Loss of social stigma
3.1 Defining Rational Behavior
Economists define a rational person as one making decisions that are optimal for them, in light of their
preferences, information, and budget constraint. This includes rationally evaluating tradeoffs within a
period of time (consumption of one good versus another) and tradeoffs over time (preferences for
consumption today versus in the future), along with accurately acting on those preferences. This
definition of rationality is not necessarily the same as used in other disciplines or everyday discussion.
Throughout this paper, we use the economic definition of rationality.
Addiction by itself does not imply that people’s actions are irrational. Becker and Murphy (1988)
developed a “rational addiction” model where consumers may choose to use an addictive good if the
anticipated lifetime pleasures of the addictive good outweigh the correctly anticipated lifetime
expenditures and health risks. Some support for the idea of rational smoking comes from the finding
that, when people know cigarette prices will rise in the future, they tend to cut back on their current
consumption, which would not be expected if smoking was driven solely by addiction (Chaloupka 1991,
Gruber and Köszegi 2001). Thus, even if people are addicted to cigarettes and regret having ever started,
their current decisions about continuing or stopping smoking need not necessarily be irrational.
Consistent with this definition, we conceptualize three groups of individuals (Table 3). The first
group consists of people who rationally decide to start using the good based on time-consistent
preferences and accurate information and expectations, and who also make rational decisions about
their consumption levels after they have started. Given the issues associated with decision-making by
young people noted above, this group is expected to be a small minority of all people who start
smoking. A second group experiments with addictive goods and “accidentally” become addicted, in the
sense that their lifetime utility (based on health and longevity as well as consumption) is lower as a
smoker than it would have been as a non-smoker. But conditional on having become addicted, this
group chooses its current consumption levels rationally. A third group starts using a good accidentally
and then smokes “behaviorally.” Some may do so due to time-inconsistent preferences that cause them
to wish to quit but find that withdrawal costs always induce them to put off cessation for another day.
Others in this group may continue smoking due to inaccurate information, expectations, or beliefs.
14
Table 3: Groups of Consumers
Group Initiation Subsequent consumption
1 Rational Rational
2 Accidental* Rational
3 Accidental* Behavioral
* Assumes these people wouldn’t have initiated had they rationally evaluated the difference between their lifetime utility with and without smoking.
In the case of smoking, there are several reasons to think that most initiation falls into the
accidental category. First, most youths who start smoking do not initially enjoy the taste of cigarettes
(Eissenberg and Balster 2000, O’Connor et al. 2005); enjoyment of smoking comes over time, co-
evolving with nicotine addiction. Rather, most people initially begin smoking for attention, to look older,
out of curiosity, to flaunt authority, or to fit in (CDC 2012, Chap. 4). Beyond the teenage years, these
considerations decline in importance, and smoking initiation drops (recall that nearly 90 percent of
smokers start before age 18). There is virtually no smoking initiation after the early 20s. Second, many
smokers say they would like to quit, and most adult smokers say they wish they had never started (Fong
et al. 2004). This near-universal expression of regret suggests that most smokers took on consumption
of an addictive good without rational forward-looking consideration of its costs, benefits and risks. Thus,
we expect the vast majority of smokers to be in Groups 2 and 3.
There are a number of economic theories that can explain why consumers may not act in
accordance with their true preferences: (1) behavior may be driven by habit and impulse more than
rational consideration; (2) preferences may not be consistent over time – for example, consumers may
be overly sensitive to short term costs relative to long term gains; (3) consumers face challenges in fully
understanding and internalizing the consequences of their consumption decisions; and (4) aggressive
marketing may influence behavior. Much of the economics literature has focused on the first two of
these.
Bernheim and Rangel (2004) develop a model with “hot” and “cold” states. In the cold state,
people behave rationally, consistent with the standard model. They understand the consequences of
their behavior and make decisions based on these consequences. When in a “hot” state, however, they
respond based on habit and current impulses, which may not be related to rational calculation. Cues
often shift people from the cold to the hot state; these may include seeing others using an addictive or
habitual good that one once used, or being in a place associated with past use of the good. Because
decisions are not consistently rational in this model, regulations can potentially improve welfare even
without providing new information.
Other models focus on time-inconsistent preferences. Numerous studies have found that people
are overly sensitive to short-term costs over long-term gains, often passing up significant long-term
benefits because the short term costs are more salient.10 For example, people have difficulty with
increasing their savings rate despite stating that they should save more, adhering to lower-calorie diets
10 For reviews, see Frederick et al. (2002) and DellaVigna (2009).
15
despite a desire to lose weight, exercising at the gym despite paying costly membership fees, and other
similar activities. In the classic example, people will continually defer signing up for a 401(k) plan,
despite the fact that doing so has very little cost and significant benefits in retirement. People rationalize
this behavior by asserting that they are not deciding not to enroll, but are simply putting off the
enrollment for a short period of time. Regulation can help correct these behavioral problems – for
example, by requiring people to decide on their 401(k) contribution by a certain date.
3.2 Conceptual Framework
We start with a formulation of why there may be excessive consumption of addictive and habitual goods
and use that to consider how regulations affect welfare. Our framework applies equally to initiation as
to cessation; we indicate in the next section how we implement each decision.
Consider people having a utility function of the form
W = Ut(at,St; ct) + β ∑ pt+i(𝐚) δiUt+i(at ,St;ct)T−ti=1 , (1)
where W is the discounted value of lifetime utility and Ut is the per-period utility function. We imagine
there are two goods: an addictive good, with current consumption at, and all other goods ct, where ct =
Y-Paat for constant income Y.11 Pa is the constant real price of the addictive good. St is the stock of
addiction entering period t – typically a weighted average of past consumption. We normalize the
weights so that at constant a over time (denoted by a̅), S̅ = a̅; that is, the value of the existing stock is
equal to the flow of annual consumption. pt+i(a) is the probability of survival to period t+i. It is
conditional on the entire past history of consumption of the addictive good, denoted by a. If only the
stock of smoking matters, a = St. More generally, smoking may affect health in a different way than it
affects addiction. As well as affecting mortality, it also raises morbidity which can reduce per-period
utility.
For ease, we assume that utility is additively separable in the addictive good and all other goods,
and that the marginal utility of all other goods is constant. This implies that Ut = v(at,St) + (Y-Paat). In the
Gruber-Köszegi (2001) analysis, v(.,.) is quadratic in the two elements. For presentational simplicity, we
assume a simpler formulation where v = v(α1at-α2St). Following standard assumptions, v(.) is assumed to
be concave.12 α1 measures the extent to which consumption of the good increases utility per period. α2
reflects withdrawal. It is the extent to which today’s utility is lower if one consumed more of the good in
the past, holding current consumption constant.
The function v(.) embodies the attributes of the product, not necessarily the product itself. In
the case of cigarettes, this includes the nicotine it delivers, the relaxation it may convey, the feel in the
11 The fact that all income is spent on the addictive and non-addictive goods implies that there is no saving or borrowing. 12 Addiction is modeled empirically as adjacent complementarity – the value of consumption today is higher if consumption was greater in the past. We capture adjacent complementarity in a slightly non-standard way. If a t increases, then St+1 increases as well. Because higher St+1 leads to greater withdrawal costs, utility is lower in t+1. Given the concavity of the utility function, this increases the return to a t+1. An alternative way of capturing adjacent complementary would be to include a term α3atSt, where α3>0 reflects adjacent complementarity. Adding such a term would not materially affect our analysis.
16
mouth and hand, and the signals that cigarettes send to others. Empirically, this means that the price
elasticity of demand for cigarettes may vary with substitutes for those attributes. For example, if e-
cigarettes convey many of the same attributes as combustible cigarettes, the price elasticity of demand
for cigarettes will rise when e-cigarettes are available.
The function v(.) may also vary with age. Teens may benefit from the rebellious image that
smoking conveys, while adults may not. It is possible that α1>0 for teens and α1≈0 for adults; this would
correspond to a situation where a never-smoking adult may not derive any pleasure from smoking.
One important case is where α1= α2. If this is true, there is no steady-state benefit to consuming
the addictive good. That is, v(.,.) is the same at any constant a̅, even a̅=0. But there are withdrawal costs
associated with moving from a higher a to a lower a, because reducing a below S̅ would be utility-
reducing. To avoid this withdrawal effect, so people may not make the change.
People discount the future in two ways. The first is the standard exponential discount rate, δ. To
avoid excess notation, we assume that δ=1. In addition, some people have a preference for current
utility over all future utility, captured in the parameter β. If β<1, the individual is time inconsistent: they
will continually delay making investments with up-front costs, even if they would agree ex ante that the
investment is a good idea. Consumers with β=1 apply the same discount between the present and the
near-future as they would between two adjacent periods in the future. Consumers with β<1 apply an
extra discount to all future periods relative to the present, so they will continually delay making
investments with up-front costs, even if they would agree when thinking about what would best benefit
them in the long-term that the investment is a good idea. Consumers with β<1 are commonly referred
to as hyperbolic (Laibson 1997).
Time inconsistent discounting will lead to suboptimal outcomes. Incomplete information about
future consequences of current actions will as well. People may not understand the health costs of the
addictive good (pt+i(a)) or how addictive the good is (α2). For simplicity, we assume people understand
the addictiveness of cigarettes (i.e., perceptions of α2 are accurate) and examine the impact of
inadequate understanding of the health cost of smoking – for example, the evidence that people do not
appreciate how sick they will be at the end of life. We denote the perception of this variable p̃t+i(a).
The individual’s optimal consumption of the addictive good can be found by maximizing utility.
This yields the first order condition:
α1v′t − α2β∑ p̃t+iv′t+i
dSt+i
dat + β ∑ Ut+i
dp̃t+i
dat = Pa (2)
current future consumption withdrawal longevity forgone benefit cost cost consumption
The first term on the left hand side of equation (2) is the current marginal benefit of consumption. That
may involve direct consumption benefits or indirect benefits such as relaxation or weight loss. It also
includes the change in health within a year – that is, health-related quality of life. The second term is the
discounted value of the future withdrawal cost that will be caused by consuming more of the addictive
good in the current period. These costs are only born for users of the good as they reduce consumption.
17
These costs decay in the future as the impact of current consumption on the future stock of addictive
capital declines (i.e., dSt+i
dat declines with increasing i). The third term is the mortality impact – the
reduction in lifetime utility from premature mortality due to smoking in period t. Note that dp̃t+i
dat<0 so
this term is negative.
At the optimum, the consumer will trade off the sum of these impacts against the cost of
increasing consumption. This cost is the marginal utility of other consumption forgone, which is given
by Pa.
Figure 1 shows a graphical version of this model that underlies the framework for valuing
benefits and costs of regulations. As is the norm, we include the monetary price and health and
longevity costs as costs, and the consumption benefits net of withdrawal as the value to the individual,
though no practical difference in results depends on whether a health harm is termed a cost or a
negative benefit. The demand curve thus reflects the first two terms on the left hand side of equation
(2), and the remaining two terms are built into the cost. Because withdrawal costs vary with past
consumption, so too will the value of current consumption. We start by assuming the individual has
some addictive stock and draw the demand curve corresponding to that addictive stock. We return to
the dynamics below.
The model can be applied for both existing users of the good and for potential initiators,
although the implementation differs between the two cases. Considering existing users first, we
delineate two types of individuals with otherwise similar characteristics (age, gender, etc.): type I
consumers who make smoking decisions in a way that is fully rational and fully informed (β=1 and
p̃t+i=pt+i); and type II consumers who have time inconsistent preferences (β<1) or misperceive how their
current actions will affect their future choices and health risks (p̃t+i≠pt+i). Relative to Table 3, both group
1 and group 2 consumers fall in the type I category; group 3 consumers are all type II.
The demand curves of the two types of consumers are shown by DI and DII respectively; these
demand curves can reflect either the share of people who use the good or the total number of units
used by those individuals, each of which will vary with price. For type I individuals, the demand curve
reflects the value of consumption to the individual – the first two terms on the left hand side of
equation (2). For type II individuals, the demand curve may differ from the true value to the extent that
people have difficulty making their actions match their intentions. If type II individuals find it hard to
reduce consumption when that is desired, DII will be farther out than DI.
There are two prices to consumption: the health cost, which we denote Ph, and the market price
of the good, Pa. For example, Gruber and Köszegi (2001) estimated that the health cost far exceeded the
monetary cost -- $30 per pack in health cost versus $5 per pack in monetary cost at that time.13 Given
these prices, consumers from the two groups will choose to consume QI and QII respectively.
QI and QII would also differ if type II individuals misperceive the full health cost to using the
good. For example, if type II consumers do not perceive any health cost to using the good, the
equilibrium will be where their valuation of the good is equal to the monetary price of the good, Pa. This
13 Gruber and Köszegi (2001). Their estimate factors in health costs of reduced life expectancy only, not reduced health during the lifetime.
18
result is shown as Q̂II in Figure 1. If they misperceive some of the cost, Q̂II will be lower, but still above
QI.
Figure 1. Equilibrium with two types of consumers
Price
Impact of time-inconsistent behavior
DI DII
Ph+Pa
Ph
Impact of misperception of health cost
Pa
Quantity
QI QII Q̂II
With some change in interpretation, Figure 1 also models the smoking initiation decision. DI
corresponds to group 1 in Table 3 – the (presumably very small) group of people who start smoking
rationally; DII reflects demand from groups 2 and 3. The figure would then show the share of people in
each group who choose to smoke, or equivalently the number of cigarettes among each type of
potential smoker.
Regulations induce some individuals to stop smoking and others to avoid initiation. The
longevity benefits of this regulation are picked up by the third term in equation (2), corresponding to Ph
in Figure 1. The relevant issue for our analysis is how to value the offset from lost pleasure of smoking.
We discuss this in the context of Figure 1.
3.3 Modeling Regulations
In carrying out its responsibilities for regulating food, tobacco, drugs, and other products, FDA may issue
several types of regulations that could affect consumers. First, some regulations change the information
available to consumers (as with ingredient labels), or the salience of the information (as with graphic
warning labels), in the interest of fostering well-informed consumer choice. Second, regulations may
require producers to change how they manufacture, distribute, or market their good, usually aiming to
better satisfy public-health or safety concerns. If such a change increases producers’ costs, the higher
costs may be passed through to consumers in the form of higher prices. Third, regulations that change
how products are marketed, distributed, or sold may require consumers to spend more time or money
19
acquiring goods. For example, limiting vending machine sales of cigarettes reduces the density of
potential places to buy cigarettes, so consumers have to spend more time finding an alternative outlet.
This is in effect an increase in the implicit price of the good, because the total cost to the consumer of
acquiring it (the money cost plus the time cost of acquiring it) has increased.
Fourth, some regulations may require companies to alter or eliminate product attributes that
people value, such as flavors or packaging, perhaps in the interest of reducing smoking initiation by
youth. Conceptually a reduction in valued attributes can also be understood as an implicit price increase
for the good. For example, if a regulation requires companies to remove flavors from cigarettes, and
people who liked flavors switched over to electronic cigarettes to continue smoking flavored products,
the implicit price of smoking rises for them, as they would now have to consume two different products
(non-flavored combusted cigarettes plus flavored e-cigarettes) to get the same cluster of attributes they
previously consumed in one.
Finally, FDA may issue jurisdictional or administrative regulations that clarify regulatory
procedures or formalize the scope of its regulatory authority (like the deeming rule). Regulations in this
category will usually have relatively small direct effects on consumers, as they usually intend to facilitate
implementation of other regulations or enable smooth functioning of regulatory processes broadly,
rather than altering consumption experiences of end-users of the product.
Economically, these regulatory activities fit into two groups: those that affect information and
those that affect prices, where the price increase may be explicit (e.g., higher manufacturing costs that
are passed along to consumers) or implicit (higher search or acquisition costs to achieve the same
benefits). We discuss each of these below. We further differentiate between the impact of regulations
on existing users and on potential new users.
3.3.1 Regulations that Affect Information
We start with regulations that increase the amount or salience of information about health effects.
Information about some of the major harms of smoking, such as risks of lung cancer and heart disease,
has been widely available for some time, making it tempting to ignore this issue in the case of cigarettes.
An important line of economics research suggests that consumers accurately perceive the mortality risks
of cigarette smoking, if not over-estimate them.14 However, it is not clear that current regulations can be
interpreted as occurring in a context of full information. Even if consumers are aware of the major
health risks associated with cigarette smoking, it is more difficult to assess whether they understand the
severity of health risks, or whether information they know remains salient enough to affect their
choices. Consumers also may not understand how population risks associated with smoking apply to
them.15 Finally, the scientific research on the health risks of tobacco products is evolving, especially for
14 As a review of research by Sloan and Wang (2008) concludes, “The economic l iterature lends no empirical support to the view that mature adults smoke because they underestimate the probability of harm to health from smoking.” See also Kenkel (2000, 1697-1699), Kenkel and Chen (2000) and Chaloupka and Warner (2000, 1595). 15 For review, see Reyna and Farley (2006).
20
novel products like e-cigarettes, and consumer knowledge may not fully reflect the evolving
understanding.16
Suppose that type II consumers have a difficult time giving up cigarettes or not initiating
smoking, but regulations help them do that (perhaps by highlighting the salience of poor health
outcomes later in life). Also suppose that after the regulation comes into effect, type II consumption is
denoted Q'II. The welfare of type II consumers would be affected in three ways. First, they would save
money on the packs of cigarettes they no longer buy. This is the right hand side of equation 2 and is
shown as the green rectangle in Figure 2 – the retail price per pack times the reduction in consumption.
The entirety of the green rectangle is a benefit to consumers, but not all of it is a social benefit. Notably,
the reduction in demand reduces tax payments to governments and sales revenues for producers, so
that part of the saving by consumers comes from a transfer of resources away from governments and
producers. While a full regulatory impact analysis would subtract losses to governments and producers
from the benefits to consumers to derive the social benefit, the full amount of the savings to consumers
is of interest here due to our focus on the benefits part of the analysis.
Figure 2. Welfare with a Salience-Based Regulatory Intervention
Price
DI DII
Ph+Pa
Ph
Quantity
QI Q'II QII
Second, there is the health benefit of reduced consumption. This is the expected health cost per
unit consumed times the reduction in units consumed, the red box in Figure 2. Third, there is a utility
change associated with reduced consumption. This includes the first two terms on the left hand side of
equation (2). Graphically, this change in utility corresponds to the blue area under the type I demand
curve for the addictive good between QII and the new level of consumption. The key is that the demand
16 For example, Oncken et al. (2005) found that over 90 percent of smokers had good knowledge of risks of cardiovascular, pulmonary, and oral problems due to smoking, but only 40-60 percent had good knowledge of other types of risks (cancers other than lung, reproductive problems, smoking-related disabilities).
21
curve to use to value this change is that of the rational consumer (type I), not the consumer with
behavioral difficulties (type II). The reason is that the rational consumer’s demand reflects the true value
of smoking to the individual.
Figure 2 shows a situation where the regulation affects consumption by encouraging consumers
to shift their smoking towards the rational standard. We can alternatively model the regulation as
affecting the information that consumers have. Figure 3 shows the situation where all intentions are
accurately acted upon, but type II consumers do not appreciate the full health cost of smoking – they
perceive health costs to be P̃h rather than Ph. With only an informational problem, there is only a single
demand curve. But there are two equilibrium quantities, QI, which reflects demand among those who
correctly perceive the health cost of smoking, and QII, which reflects demand among those with
inaccurate perceptions. A regulation that increases the information available to type II consumers – for
example, increasing their perception of health costs to P̃’h – will reduce their consumption to Q'II. The
welfare calculation will be quantitatively the same as the previous case because the market price is the
same and the correct health benefit to consider is the true health cost (Ph), not the inaccurate initial
impression of health costs.
Figure 3. Welfare with a Regulation that Affects Knowledge of Health Harms
Price
Ph+Pa
Ph
P̃’h+Pa
P̃h+Pa
Quantity
QI Q'II QII
3.3.2 Withdrawal versus Sustained Utility Losses
Figures 2 and 3 are set in a single time period. The dynamics are more complex than this static analysis,
however, because the utility consequences of changing consumption vary over time. In the short run,
the biggest utility consequence of reduced consumption is withdrawal costs, which are high at first but
smaller in later years. Over time, the costs are more heavily focused on foregone consumption value,
which are initially lower but need not decline.
Analytically, this distinction is seen in the first two terms in equation (2). Imagine a consumer
who has been consuming ahigh for a number of years – long enough for the stock of past consumption
(St) to equal ahigh. The annual benefit of consumption is then v((α1-α2)*ahigh). Now suppose regulation
induces the individual to switch to alow. The steady-state benefit of consumption is v((α1-α2)*alow). The
loss in steady-state utility from the reduction in smoking is approximately (α1-α2)*v'*(alow-ahigh). If α1≈α2 –
that is, the major benefit of continuing to smoke is avoiding the withdrawal cost – there is very little
22
steady-state loss to reduced consumption. But moving to that point involves withdrawal costs, which
are given by ∑ v (α1alow − α2St+i). These costs may be significant.
To understand the situation graphically, we need to consider the dynamics of demand. If a
person stops or cuts back on smoking, their stock of addictive capital declines in the future. This will lead
to a shift down in the demand curve (the same utility is achieved at lower consumption). This process
continues until the addictive stock reaches its new, lower level.
Figure 4 shows the new equilibrium. The demand curve D’I is the value of cigarette consumption
in steady state, when the stock of addictive capital has declined to its long-run level. The downward
slope to the demand curve is consistent with α1>α2; some type I individuals value the addictive or
habitual good, even beyond avoiding withdrawal. Even still, the value of consumption is lower than in
the short-run demand curve, DI because in the long-run there are no withdrawal costs. As is apparent,
the steady-state welfare loss is much smaller than the short-run welfare loss, because withdrawal costs
are only experienced in the short-run.
Figure 4. Welfare Accounting for Withdrawal Costs
Price
DI DII
Ph+Pa
Ph
Withdrawal cost
D’I Steady-state loss
Quantity
QI Q'II QII
Distinguishing between withdrawal costs and steady-state welfare losses cannot be done
without knowing α1, α2, and the evolution of St. Theoretically, we might expect that the division
between withdrawal costs and long-run utility offset would differ across addictive and habitually
consumed goods. In the case of smoking, a natural benchmark for the long run is that the only cost of
regulatory-induced smoking reduction is the withdrawal cost, and that smoking itself conveys no utility
benefit (α1=α2). Thus, the long-run per-period utility offset is zero. This is consistent with the view that
23
few people ever start smoking as adults – the benefits are not sufficient to warrant it.17 Another
rationale for this benchmark is that some people may benefit from regulatory induced-changes in
consumption even more than the health consequences suggest. Many smokers who quit experience
improvements in self-worth or reductions in self-loathing after they quit (Piper et al. 2012). These costs
are not shown in Figure 4 but are real. It is unknown how many people experience improvements in
self-regard relative to the share that experiences losses from not smoking at their former level.
In practice, we use several methods to approximate the short run utility loss of reduced
consumption of cigarettes, including using physiological and psychological data on the process of
withdrawal to estimate the quality of life implications of changes in smoking and examining long-run
shares of type I and type II consumers who smoke.
3.3.3 Regulations that Affect Price or Product Attributes
A second category of regulations is those affecting the product price, explicitly or implicitly. As
mentioned, regulations that affect producers’, distributors’ or retailers’ costs may increase the explicit
price of the product paid by consumers. Regulations that restrict marketing channels for tobacco
products or remove valued product attributes implicitly increase the price consumers pay to acquire the
same product as before or to acquire the same bundle of product attributes as they did previously. Thus,
the point of departure for analyzing this category of regulation is to estimate how the regulation affects
the product’s effective price.
The economics of an implicit price increase are shown in Figure 5. Suppose that a regulation
increases the implicit price of the product from Ph+Pa to Ph+Pa+PI. The welfare consequences for those
who choose to stop consuming are the same as in Figure 3: there are cost savings, health benefits, and a
utility loss. There is an additional cost, however, corresponding to the higher implicit prices paid by the
type I and type II consumers who continue to use the good. For example, if regulations require people to
smoke outdoors, the cost of being outdoors in cold climates is a cost that needs to be counted. Both
groups suffer a loss equal to the implicit price increase multiplied by the number of units they consume
in the new equilibrium. Here there is also a loss for type I smokers, who forgo utility benefits for the
units of the good they no longer consume because of the higher price.
17 Note that this is not definitive, however. It may be that all the people who would want to smoke initiate as teens. Empirical evidence about whether people deterred by tobacco-control interventions from smoking as teens have risks of taking up smoking as young adults is limited.
24
Figure 5. Welfare with Regulatory Intervention that Increases the Product Price
Price
Loss for type I consumers
Loss for type II consumers
DI DII
Ph+Pa+PI
Ph+Pa
Ph
Quantity
QI Q'II QII
One case of particular note is regulations that affect the attributes of goods, for example those
changing flavors like menthol or other characterizing flavors.18 The effective price increase from such a
regulation can be determined by the change in demand combined with the demand elasticity;
alternatively the effective price may be estimated directly then used to project the change in demand
expected to be caused by the regulation. This price increase would then be applied to people for whom
the flavor was a key part of the value of consumption. Whether such regulations are beneficial or
harmful on net depends on the benefits of reduced consumption relative to the cost of raising prices for
goods that people would otherwise consume.
3.4 Summary
There have been a variety of estimates of the welfare consequences of regulations affecting addictive
and habitual goods, particularly tobacco use. On the one hand, FDA (2011) and Ashley et al. (2015) argue
that 10 to 90 percent of the health benefits of smoking could be offset by the lost pleasure from
smokers consuming less. On the other hand, Chaloupka et al. (2015) argue that because most smoking is
initiated while people are teens, when decisions are unlikely to be rational, there should be no utility
offset for smoking reduction.
Our analysis shows that neither of these analyses is complete. Relative to the approaches in the
literature, we make three innovations. First, we show that reduced spending on smoking should be
included in the benefits to consumers along with health benefits. Reducing consumption of addictive
and habitual goods saves consumers a significant amount of money that would otherwise have been
18 The 2009 TCA banned cigarettes with characterizing flavors (clove, chocolate, pineapple, grape, etc.). FDA has also considered whether to ban menthol cigarettes.
25
spent on the addictive or habitual good. Not all of this saving is a social benefit because it comes out of
sales revenues of companies and tax revenues of governments. But the money savings properly belong
in the benefits to consumers.
Second, we divide the utility offsets of reducing consumption into two components: the short-
term costs of withdrawal, and the steady-state benefits of not consuming a good. The withdrawal costs
are real; most smokers make several attempts before they quit the habit. After withdrawal has been
achieved, however, the losses from not smoking on an ongoing basis may be much smaller. It is
important to estimate both the short-term and the steady-state utility costs of regulatory-induced
changes in consumption.
Third, there may be additional costs for people who continue to consume the addictive or
habitual good even after the regulatory intervention. Regulations that increase the cost of the addictive
good – for example, making it less attractive or more difficult to obtain – impose real costs on these
individuals and these costs need to be counted. Not all regulations affect cost for continuing users, and
some impose costs for only some individuals. But costs that are imposed on consumers need to be
understood and evaluated.
4.0 An Empirical Approach to Welfare Evaluation
Modeling the impact of regulations empirically is complex when observed behavior is not a full guide to
utility. Chetty (2015) lays out three approaches to welfare analysis in such a setting. First, analysts can
measure actual utility as it is experienced (termed ‘experienced utility’) and use that instead of the more
conventional ‘decision utility.’ For example, an individual may believe they will be worse off if they stop
smoking, but in actuality may be better off. The actual experience will measure this better than pre-
cessation predictions. Some research examines how smokers’ subjective well-being changes when they
quit – an approach that could potentially be useful for directly measuring utility offsets to health
benefits of regulations.19 However, methods of quantifying changes in subjective well-being and
translating them into monetary values are not sufficiently well-developed at present to be able to use
results from existing work in cost-benefit analysis.20
Second, analysts can parameterize an explicit model of utility and use that to estimate welfare
losses. This is the approach taken in Gruber and Köszegi (2001) and approximated in Ashley et al. (2015).
A key problem with this approach is that calculations that result from it are highly sensitive to the
parameterization of the time consistency problem and to assumptions about smokers’ expectations and
beliefs.21 It also analyzes utility losses of existing smokers only; if a regulation works in part by deterring
19 Relevant studies include Shahab and West (2009, 20120), Piper et al. (2012), Taylor et al. (2014), Wang et al. (2014), and Weinhold and Chaloupka (2014). 20 A question of concern in a cost-benefit context is whether changes in subjective well-being associated with self-initiated voluntary quits may not accurately represent quits induced by regulations. In part to address this concern, Gruber and Mullainathan (2005) examined how increases in cigarette excise taxes affected subjective well-being among those with a propensity to smoke in the U.S. and Canada, finding a positive effect. 21 Explicit models of addictive consumption are also highly stylized and hard to distinguish between empirically, so
that the appropriateness of the model to the issue at hand is an untestable maintained hypothesis.
26
initiation, and utility losses of deterred initiators are small compared to those of existing smokers,
analyzing the latter only may overstate utility loss in the full pool of people affected by the rule.
Third, analysts can find groups of people believed to be making rational decisions and compare
their behavior to that of the entire population. The difference in behavior between these groups is then
a guide to the impact of regulations that encourage people to shift their behavior towards the rational
benchmark. Largely for data reasons, we focus on the third of these methodologies in this paper. The
appendix discusses other approaches that could also be used to quantify non-health effects in a cost-
benefit context.
4.1 Impact of Regulations on Cessation
In this section, we address the empirical question: if some smokers are induced by regulation to stop
smoking, how do we measure the utility consequences of the consumption change other than those
associated with better health and longer life expectancy?
Our analysis is based on the demand curves analyzed above, so we start with empirical
formulations of those curves. The data on smoking that we employ are from the 2010-11 Tobacco Use
Supplement to the Current Population Survey (CPS-TUS), conducted by the U.S. Census Bureau. These
data contain information on adults’ current and former smoking status, and various measures of
addiction and desire to quit smoking.
We start with the short-run demand curves – demand curves among those who are current
smokers of cigarettes. The average smoker in the CPS-TUS consumed 230 packs of cigarettes annually,
and the average retail price of cigarettes in 2010-2011 was $5.43 (Orzechowski and Walker 2012). Thus,
the demand curve for the average smoker must pass through this point. Consistent with existing
research on the elasticity of demand for cigarettes (Chaloupka and Warner 2000, Gallet and List 2003),
we assume a price elasticity of -0.3. As in our theoretical description, we linearize the demand curve
around the equilibrium values. This implies a slope of dP/dQ = -0.079. We assume this slope applies for
both type I and type II smokers.
Delineation of type I and type II consumers
To measure the utility cost of smoking, we need to differentiate between type I and type II
smokers. There is no perfect division of the population into these two groups, since we do not know
whose smoking behavior is rational or not (in an economic sense) for large segments of the population.
In the absence of better information, we make two main cuts at the delineation between type I and type
II smokers, shown in Table 4, with some additional insights from a third. Our first distinction is between
those who have clear addiction to cigarettes and those who do not. This division of the population is
consistent with the methodology of the Australian Productivity Commission (2010), which compared
gambling rates among problem gamblers and non-problem gamblers. A widely used measure of nicotine
addiction is whether the person has their first cigarette within one-half hour of waking (CDC 2000). We
define type I smokers as those who do not have their first cigarette within one-half hour of waking, and
type II smokers as those who do.
27
By this definition, 56 percent of current smokers are type I and 44 percent are type II.22 Type I
and II smokers differ in systematic ways. Compared to type II smokers, type I smokers are younger
(average age of 41 vs. 45), better educated (15 percent with a college degree vs. 9 percent), and higher
income (18 percent with a family income above $60,000 vs. 14 percent). Type I smokers also consume
fewer cigarettes than type II smokers: 152 packs per year compared to 327 packs per year. Some of this
difference is due to differences in demographics between the two groups, but not the bulk of it; for
example, using a Tobit model to control for differences in demographics across groups, the unadjusted
difference of 175 packs per year corresponds to 154 packs per year (results not shown).
Our second empirical proxy for type I and type II smokers uses demographic information that is
generally associated with more rational economic behavior. We consider type I smokers to be people
age 30-45 having a college degree or more, and type II smokers as those over age 45 or age 30-45
without a college degree.23 Age and education pick up both access to information and the ease of acting
on that information. Individuals in the 30-45 age range began smoking well after the health risks of
smoking became well-publicized and thus had better information with which to make decisions.24
Further, having a college education correlates with the time and ability to process scientific information
about risks and with greater resources enabling people to carry through on forward-looking decisions.25
Since education decisions are not yet final for much of the population below age 30 and full economic
rationality for part of that group is suspect (as noted above), we omit them from this analysis. Not
surprisingly, smoking decisions vary greatly across these groups. Type I smokers smoke 162 packs per
year, compared to 250 packs for type II smokers.26 Type I smokers are only 6 percent of the smoking
population age 30 and above. Clearly this delineation does not fully reflect the type I population but
rather captures a segment whose behavior may best approximate the rational benchmark. To the extent
that many other people are rational smokers but are grouped as not fully rational, this will bias us away
from finding any large difference between the groups, thus overstating the potential losses from
smoking cessation.
22 When more detailed measures of nicotine dependence are used, the share of smokers categorized as nicotine dependent is higher (e.g. SAMHSA 2013). 23 For evidence on relationships between age, education, and decision-making, see Choi et al. (2014). 24 In 2010, for example, people in this age range turned 16 in the years between 1981 and 1996. According to Gallup poll data, 45 percent of the adult population believed smoking was a cause of lung cancer in 1958. By 1969, the share had increased to 71 percent. It has been above 90 percent since the early 1990s (Saad 2002). 25 Because the vast majority of smokers begin smoking while still in their teens, this ability may affect choices about cessation more than choices about initiation. Nonetheless, if teens who eventually obtain college degrees begin to acquire this ability as teens, their choices related to smoking initiation may also have been relatively well-informed and forward-looking compared to other teens. 26 Because we omit the population under age 30, the overall average of cigarettes smoked is slightly higher using this methodology than in sorting people by the degree of addiction: 245 vs. 230 packs annually. These are sufficiently close that we assume the same demand elasticity in both settings.
28
Table 4: Alternative Delineations of Type I and Type II Smokers
Degree of Addiction Age and Education Quit Intentions
Type I Type II Type I Type II Type I Type II
Not nicotine dependent
Nicotine dependent
Age 30-45 and 4-year college degree or more
Age>45 or age 30-45
and less than 4-year college
Quit smoking or smoking
without trying to quit
Trying to quit
Current smoking status % in current smoking population
55.9% 44.1% 5.6% 94.4% --- ---
# packs per current smoker
152 327 162 250 --- ---
Long-run cessation
% of ever smokers who currently smoke
--- --- 34.4% 42.0% 32.4% 100%
# packs per ever smoker
--- --- 56 105 89 173
Initiation % ever smoker 19.9% 39.8% % with sustained initiation
--- --- 6.9% 16.7% --- ---
# packs per potential initiator
--- --- 11 42 --- ---
We now consider two types of regulatory interventions: a first which helps smokers align their
actions with their preferences but without affecting cigarette prices (perhaps increasing the salience
with which health information is conveyed); and a second that can be thought of as imposing an implicit
tax on smoking. For concreteness, we assume that each intervention reduces demand among smokers
by 10 percent -- for example, by leading 10 percent of existing smokers to quit. In the case of a salience
intervention, all of the reduction in demand would occur from type II individuals. In the case of an
implicit price increase, both type I and type II individuals would be affected.
Impact of a salience-based intervention
The welfare effects of a policy that helps type II smokers align their behavior with their true
preferences can be measured in three steps, which are shown in the top left panel of Table 5. The first
impact is the health benefits, including both improvements in morbidity and mortality. A large research
literature shows that smoking adversely affects length and quality of life (CDC 2014a). As a basis for
gauging how the magnitude of utility offsets compare to health benefits, we compute standard
estimates of benefits to a smoker of quitting now relative to continuing to smoke, in terms of both
29
reduced mortality only and mortality and morbidity together.27 Valuing health benefits in dollar terms
requires two additional parameters: the discount rate and the value per quality-adjusted life year
(QALY). We use the two discount rates, 3 percent and 7 percent, recommended by OMB (2003) in its
guidelines for conducting cost-benefit analyses of federal regulations. In line with a recent review of
high-quality research on values per statistical life (VSL) (Robinson and Hammitt 2015), we anchor our
estimates of the value per QALY in VSLs of $4.3 million and $9.3 million; accounting for age-related
changes in the health-related quality of life, these estimates imply values per QALY of $220,000 and
$480,000 at a 3 percent rate, and $370,000 and $790,000 at a 7 percent rate. For a 35-year old smoker,
the results (in the first row of Table 5) show that continuing to smoke has health costs of about
$200,000 to nearly $1.5 million for a 35-year old smoker, depending on the assumptions used.
Second, there is the money saved. Smokers who quit save $5.43 for every pack they do not buy.
Multiplying times the average packs per type II smoker in Table 4, the money savings is $1,776 per year
in the addiction-based delineation and $1,358 per year in the age/education delineation. For simplicity,
we take the central value to be about $1,500 per year.
Finally, there is the utility offset from reduced smoking. The first step in estimating the utility
offset from the demand curve is to estimate the implied value of cigarettes for type I consumers. Recall
that our goal here is to measure the value of smoking to type I individuals at the current level of
consumption of type II individuals (see Figure 2). We can calculate this using the slope of the demand
curve and the gap in cigarettes consumed between type I and type II consumers. Because cigarettes
consumed differ in the addiction and age/education frameworks, the implicit valuation for the type I
consumers at that point is slightly different.
The exact dollar value depends on the health costs that type I smokers perceive. To approximate
expectations of the health cost per pack for type I smokers, we use estimated effects on longevity and
health described above, divided by the average number of packs smoked per year computed from the
2010-11 CPS-TUS. As shown in Table 5, when both mortality and morbidity costs are taken into account,
estimates of the health cost per pack range from $48 to $174, depending on assumptions about the
discount rate and value per QALY. Given the slope of the demand curve, we can determine how much
lower prices would need to be for type I consumers to smoke at the level of type II consumers. 28 We
estimate the price reduction to be $7-14 per pack. Multiplying the implied value of a pack of cigarettes
by the number of packs given up yields utility losses ranging from $5,500 to $54,000 for smokers who
quit (the middle block of table 5). Focusing on results with both morbidity and mortality costs of
smoking, we take the central value to be $25,000.
27
Notes to Table 5 give data sources and methods. 28 The necessary price change is given by dP/dQ * (Q II-QI).
30
Table 5. Estimated utility losses from quitting using the demand approach for a 35-year old smoker
VSL of $4.3 million VSL of $9.3 million
3% discount rate 7% discount rate 3% discount rate 7% discount rate
Health benefits included in estimate
Mortality yes yes yes yes yes yes yes yes
Morbidity no yes no yes no yes no yes
Estimated health benefits of quitting now
Expected lifetime health benefits $462,400 $669,400 $209,000 $403,900 $1,008,800 $1,460,500 $446,300 $862,300
Implied health cost per pack
$55 $80 $25 $48 $120 $174 $53 $103
Utility loss of existing smokers who quit
Addicted v. not $15,300 $23,400 $5,400 $13,000 $36,500 $54,200 $14,600 $30,900
Age/education $13,400 $19,600 $5,900 $11,600 $29,600 $43,100 $12,900 $25,400
Utility loss as % of lifetime health benefit
One-year loss:
Addicted v. not 3% 4% 3% 3% 4% 4% 3% 4%
Age/education 3% 3% 3% 3% 3% 3% 3% 3%
Ongoing loss of 25% of first-year value
Addicted v. not 23% 24% 18% 22% 26% 25% 23% 24%
Age/education 20% 20% 20% 19% 21% 20% 20% 20%
Notes: Values per statistical life are from Robinson and Hammitt (2015). Values per quality-adjusted life year (QALY) are
adjusted to reflect age-related changes in health-related quality of life (Hanmer et al. 2006). In analyses including morbidity, smoking is assumed to reduce the QALY by .05 (from Jia and Lubetkin 2010); after a person quits, the discount
is assumed to fade out over the next 5 years. Expected lifetime health benefits of quitting are co mputed by taking the
difference between the expected present discounted value of future QALYs if the person quits, to that if the person
continues to smoke until death. Data on mortality risks are taken from Arias (2014, Table 1); data on relative risks fo r current and former smokers are from Vugrin al. (2015, Appendix). To compute implied health cost per pack, we divide the
expected lifetime health benefits of quitting (i.e. the cost the person would incur by continuing to smoke) by the expected
number of packs the person would smoke: this is the expected additional number of years the person would smoke, times
232 packs per year (average packs per year, computed from the CPS-TUS). In estimates allowing for the possibility of ongoing utility losses from quitting, the loss is assumed to be 100% of the dollar value in the year when the smoker quits,
50% the next year, and 25% thereafter.
Withdrawal costs and steady-state utility loss
The component of the utility offset due to withdrawal will decline over time, as the stock of
addictive capital declines. Estimating the component of this cost which is due to withdrawal is difficult.
We make several passes at this.
A first pass is to see whether there are significant numbers of type II individuals for whom the
marginal value of smoking seems to be entirely to avoid withdrawal. Evidence suggests this is the case.
As noted above, two-thirds of current smokers say they want to quit, and 52 percent have made a quit
attempt in the past year. Likely because of high withdrawal costs, most of these attempts have been
unsuccessful. One interpretation of this evidence is therefore that about half the current smokers smoke
only because the cost of withdrawal is high.
A second way to estimate the withdrawal cost is to estimate the QALY losses from symptoms
frequently encountered during nicotine withdrawal and value those QALY losses in dollars. People who
quit smoking may have a combination of anxiety, depression, headache, insomnia, and other
symptoms. Research suggests that symptoms are most acute in the first month, and then decline
31
progressively for most of a year (Hughes 2006). By one year post-quitting, there are few physiological
symptoms of withdrawal. Since anxiety is among the most common side effects, we parameterize the
utility loss as equivalent to a clinical case of anxiety. Sullivan et al. (2005) estimate a decrement of
0.0421 in health-related quality of life resulting from anxiety. We assume the full decrement occurs in
the first month of smoking cessation, then it gradually phases out over the next year. 29 Adding up over a
year, this implies that the QALY loss from a single successful quit attempt is 0.0087.
Quitting smoking often takes multiple attempts. The majority of smokers (75-80 percent)
attempting to quit relapse within 6 months, and relapses may continue to occur even after years of
abstinence (Zhou et al. 2009). Hughes et al. (2004) estimate 10 to 14 attempts to quit based on a 5
percent or 3 percent success rate, with 50 percent of smokers quitting at some point. Data from the
2010-11 CPS-TUS show that 60 percent of everyday smokers who had made a quit attempt in the past
year made two or more attempts.30 We thus assume two attempts per year and a total of 10 attempts
before a successful quit, so that the average successful quit occurs over 6 years.
Failed quit attempts are likely to be shorter and less distressing than an eventual successful quit.
Using data on the length of unsuccessful quit attempts (Hughes 2006), the average failed quit is
assumed to involve approximately one-fourth of the QALY loss smokers experience from a successful
quit. We assume this value applies to all failed quit attempts. The combined QALY loss from the
sequence of failed attempts before a successful quit is thus .02175 (undiscounted).31
Using the values per QALY discussed above, the net result is that the cost of withdrawal
symptoms from smoking cessation ranges from $6,200 with a $220,000 value per QALY and 3 percent
discount rate, to $20,100 for $790,000 value per QALY and a 7 percent discount rate. Given the
shortness of the time period, the discount rate matters less than the VSL used. We take the central value
of the withdrawal cost to be $12,500. By comparison, if we had assumed that withdrawal was equivalent
to depression rather than anxiety, the utility cost of withdrawal would be about $18,600. 32 A reasonable
estimate is likely in this range.
29 We assume the full QALY loss occurs in the first month of cessation and phases down over the next 11 months, taking one-half of its initial in the second month, one-quarter in the third month, one-eighth in months four through six, and one-sixteenth in months 7 through 12. 30 Authors’ calculations using sample weights. 31 Five years of two attempts per year, with each failed attempt entailing a QALY decrement of (.25)*.0088 32 Depression is estimated to have a quality of l ife decrement of 0.0625.
32
Table 6. QALY loss due to withdrawal, by VSL and discount rate
VSL of $4.3 million VSL of $9.3 million
3% discount
rate
7% discount
rate
3% discount
rate
7% discount
rate
Value per QALY $220,000 $480,000 $370,000 $790,000
QALY loss in years of unsuccessful quit attempts
(years 1-5) $965 $2,105 $1,625 $3,464
QALY loss in year of successful quit (6th
year) $1,930 $4,210 $3,245 $6,929
QALY loss of full sequence of quit attempts $6,755 $11,665 $14,735 $24,250
Present discounted value of full QALY loss from
withdrawal $6,200 $9,700 $13,560 $20,100
This estimate of QALY losses due to withdrawal symptoms is close to our estimates of lost utility
from the demand-curve approach. At our central estimates, the withdrawal symptoms ($12,500-
$18,600) are about one-half to three-quarters of the utility loss of smoking cessation ($25,000). Some
part of the remaining one-quarter to one-half may represent other transitory utility costs of quitting
smoking (for example, missing the sensory experience of smoking at accustomed times and places;
Piasecki et al. 2010), and another part may constitute a steady-state loss for people who continue to
miss smoking after they quit.
Economic theory suggests that the people most likely to quit in response to regulation are those
for whom the steady-state loss is smallest. All else equal, people with the lowest cumulative costs are
most likely to withdraw when given an incentive to do so. Thus, our expectation is that the remaining
one-quarter to one-half of the utility loss will typically be over when withdrawal has run its course. As a
sensitivity test, though, below we consider the impact if one-quarter of the utility loss reflects steady-
state loss.
A third way to examine the importance of withdrawal costs is to examine the long-run smoking
behavior of type I and type II individuals. To the extent that the steady-state value of smoking is low, the
divergence between type I and type II consumers should grow over time, as type I individuals should
reduce smoking at a greater rate than type II individuals. In fact, this is true. Using our age/education
delineation of the type I and type II populations, only 34 percent of ever smoking type I individuals
currently smoke, compared to 42 percent of type II ever smokers (Table 4). When combined with the
smaller number of cigarettes smoked, average current consumption is half as large for type I ever
smokers compared to type II ever smokers.
A final way to gauge the value of steady-state utility loss is to measure the smoking rate among
those for whom the health costs are low. If smoking has high implicit value, such individuals should be
particularly likely to smoke. An example of this approach comes from the work of Oster et al. (2013),
who study the behavior of people at high risk for Huntington’s disease. People who have a parent with
Huntington’s have a 50 percent chance of inheriting the disease. Those with the disease are extremely
unlikely to survive to old age; thus, the health consequences of smoking for this group are lower. Oster
et al. examine the subsequent smoking behavior of people with ex ante identical risk, some of whom
learn they have Huntington’s and others who learn they do not. They show that among ever-smokers,
the smoking rate of those who learn they have the disease is 69 percent higher than the rate among
33
those who learn they do not have the disease. This finding suggests some ongoing utility loss for former
smokers, though it is clouded by the fact that smoking is viewed as reducing anxiety, which is likely to be
higher among those who test positive.
Given this range of data, we present several estimates of the share of the utility loss due to
withdrawal relative to health benefits of quitting. Our first estimate is the most straightforward: a large
number of people report wanting to quit smoking but are unable to overcome the withdrawal cost; we
assume that the marginal quitter who quits as a result of a new regulation is one for whom the utility
loss ends after withdrawal is complete. In this case, the full utility loss occurs in the year when the
smoker quits and is zero thereafter. This is not true for all individuals. Thus, we also consider a situation
where one-quarter of the initial loss persists in the steady-state as a bound on what the extent of the
loss could be if some people who quit continue to miss smoking after withdrawal has passed.
The last part of Table 5 shows the ratio of the utility costs of quitting relative to estimates of the
morbidity and mortality benefits from quitting for a 35-year old smoker. Although utility losses from
quitting are relatively large in the short-run, they wind up being small relative to estimated benefits
from improved health and longer life expectancy. When utility costs are assumed to be entirely due to
withdrawal, the utility losses represent about 3 percent to 4 percent of lifetime health benefits. Under
the assumption that one-quarter of the value of the initial loss is ongoing, the offsets would represent
20 to 25 percent of the value of the lifetime health benefits. Whether a given regulation would result in
an expected offset ratio towards the lower or higher end of this range depends on characteristics of the
regulation; only if the regulation cuts fairly deeply into the smoking population would we expect to see
an offset ratio towards the higher end of the range. Even in the case where a regulation induces non-
marginal quitting, the ratio of utility loss to health benefits falls at the low end of the 10 to 90 percent
range given in previous work.
Impact of implicit price increases
A regulation that also affects price would have an additional utility offset for existing smokers
who continue to smoke, because they will pay higher prices for those cigarettes. Using the demand
curves above, reducing demand by 10 percent would require an effective price increase of $1.78 per
pack in the addiction-based delineation or $1.93 in the age/education delineation. We calculate the
welfare loss from this by assuming that all continuing smokers pay the higher price – though the extent
to which this is true for all smokers, as opposed to some smokers, is an empirical question. For type II
consumers, the welfare loss is the price increase times the equilibrium number of packs smoked (225 or
304 per year per continuing smoker after the price increase, depending on the delineation method
used), so total losses sum to $435 or $543 per continuing smoker per year. For type I consumers, the
loss is analogously defined but also includes a loss associated with the decline in packs consumed (23-24
packs), together amounting to approximately $251 or $289 per smoker per year. Taking weighted
averages across the two groups, this increased spending amounts to $375 or $427 per continuing
smoker per year, or approximately $400.
34
Table 7. Impact of implicit price increases on continuing smokers
Delineation method
Addiction-based Age/education
Price increase associated with 10% decrease in demand
$1.78 $1.93
Type I $251 per year
(129 packs)
$289 per year
(137 packs)
Type II $543 per year
(304 packs)
$435 per year
(225 packs)
Average $375 per year $427 per year
4.2 Valuing Effects on Initiation
The analysis of initiation is conceptually similar to that of cessation: we estimate the cost savings and
utility offsets to those who are deterred from initiating, along with any additional costs to those who
initiate even after the regulation. The difficult question is whether people who are deterred from
smoking experience any utility loss.
We can gain insight into this question by examining differences in smoking initiation and
cessation between type I and type II consumers as identified from the demographic delineation. To
focus on initiation, we consider in both cases the population aged 30-45, who came of age when health
risks of smoking were well-known and thus had better information from which to make decisions. Thus,
we can approximate the informed-population’s smoking decisions. We can only estimate initiation rates
by demographics, since we do not know initiation rates by potential to become addicted.
As shown in Table 4, these two groups have very different initiation patterns. Eighty percent of
type I individuals never smoked, compared to only 60 percent of type II individuals. Further, most of the
type I individuals who started smoking subsequently quit; less so among type II individuals. The rates of
current smoking are about 7 percent for type I individuals and 17 percent for type II individuals. As the
subsequent decision to quit probably better aligns behavior better with underlying preferences than the
decision to ever initiate, we estimate that the population smoking initiation rate that accurately reflects
underlying well-informed standard preferences is about 7 percent annually. As noted above, even this
estimate may be too high, since some of the highly educated young smokers may have initiated
“accidentally” in their teens and now would prefer to quit – group 2 in Table 3. Fifty percent of type I
smokers in the 30-45 age range say that they are seriously considering quitting within the next 6
months, and 33% report a high overall interest in quitting (8-10 on a scale of 1-10). Thus, the “rational”
smoking rate not conditional on having started may be much below 7 percent.
Since there is no withdrawal for non-initiators, the relevant cost is the steady-state loss from not
consuming cigarettes. That is, given that 7% of type I individuals currently smoke, what can we infer
about the value of smoking for the person who would be the 17th percentile of type I smokers?
This difference is again very large. To match the smoking share of type II individuals, demand
among type I individuals would need to rise by 140%. There are differing estimates in the literature
about the price elasticity of youth initiation, with studies ranging from essentially 0 to as high as -0.8
(DeCicca et al. 2002; Carpenter and Cook 2008; Nonnemaker and Farrelly 2011). If we assume that the
35
demand elasticity is the same as for our cessation analysis, the monetary price would have to be
significantly negative for type I consumers to match the initiation rate of type II consumers. If the
“content” quit rate is only half as high as the actual quit rate, the required price would fall even more.
And if the youth smoking elasticity is even lower, as recent studies suggest, the required price would be
lower still.
Given these estimates, it seems implausible that people far from the margin of smoking value
cigarettes highly. This is consistent with the prediction that, because people deterred from starting to
smoke never develop a special taste for tobacco products, they are able to get equal or better
satisfactions from consuming other products, so a regulation that deters them from starting to smoke
entails no utility loss. Therefore, the weight of the evidence suggests that analyses should assume no
lost utility for prevented initiation among type II individuals. The health benefit and money saved would
then be the full benefit.
Table 8 summarizes the estimated effects of the two types of regulations on potential initiators.
Again the information-related regulation affects potential type II smokers only, while regulations that
change the effective price of smoking affect both groups. As before, the health benefits per deterred
initiator would be estimated using projections of reduced mortality and morbidity (along with other
factors like reduced secondhand smoke). The money savings from reduced spending on cigarettes is
again $5.43 per pack, or about $1,350 per deterred initiator per year.
Table 8. Estimated effects on potential initiators
Deterred initiator People who still initiate
Information-based regulation
Money savings $1,350 per year 0
Lost utility 0 0
10% increase in effective price
Money savings $1,350 per year 0
Lost utility 0 -$275 per year
The analysis of how a price- or attribute-based regulation would affect initiation also requires
accounting for the higher price that people who choose to smoke even in the face of higher prices will
pay. A price increase of $1.40 is needed to reduce quantity of cigarettes consumed by potential
initiators by 10 percent. %.33 The welfare loss from this would be about $275 for each continuing
initiator each year ($1.40 x ~200 packs), assuming all continuing initiators paid the higher cost. This
offsets some, but not all, of the health benefits and cost savings.
33 From Table 1, average packs per year are 11 and 42 for type I and type II potential initiators respectively; with the two group’s shares of potential initiators being about one-third and two-thirds, average packs for all potential initiators is about 33. Again taking the demand elasticity to be -0.3, the fact that the demand curve passes through the point {p=$5.43,q=42} implies dq/dp=-2.33. Then the price increase that would decrease average packs per potential initiator by 10% (3.33 packs) is 3.33/2.33 =$1.40.
36
4.4 Population Estimates
Table 9 provides a summary of the non-health benefits of the two types of regulations for the specific
types of people who may be affected. The total benefits of a regulation are given by these amounts,
multiplied by the number of people affected in each category, summed across people for each year of
the time horizon of the analysis, then discounted to the present using appropriate discount rates (3
percent or 7 percent). Total non-health benefits are computed by summing the annual benefits over the
relevant time horizon (e.g. which may be 10- or 20-years for a jurisdictional or administrative regulation,
or 20- to 100-years for a regulation that would significantly affect smoking rates).
For either information-based or price-based regulations, smokers who quit realize money
savings of about $1,500 per year and dissuaded initiators have savings of about $1,350 per year,
occurring on an ongoing basis. Smokers who quit experience a one-time utility loss of $12,500 to
$25,000 in present value terms, depending on the approach we use. We expect most smokers who quit
as a result of a regulation to have utility losses that run their course after withdrawal is over. However, if
a regulation cuts further into the pool of existing smokers, some may experience ongoing utility losses;
our estimates of QALY losses from withdrawal symptoms suggest ongoing utility losses of less than one-
quarter of the initial loss. For regulations that increase the explicit or implicit price of smoking, smokers
who continue to smoke or initiate smoking have ongoing utility losses of $400 or $275 per year
respectively.
Table 9: Summary of utility offsets for existing smokers and potential initiators*
Regulation Type Information or salience intervention
only Regulation that increases effective
cigarette price Existing smokers
Induced quitters Smokers who continue to
smoke Induced quitters
Smokers who continue to
smoke Health benefits yes no yes some Money saving ~$1,500 per year --- ~$1,500 per year --- Util ity loss (gain) One-time $12,500 to $25,000 --- $12,500 to $25,000 ---
Ongoing Possible loss of
$6,250 ---
Possible loss of $6,250
-$400 per year
Potential initiators Dissuaded initiators People who stil l
initiate Dissuaded initiators
People who stil l initiate
Health benefits yes no yes some Money saving ~$1,350 per year --- ~$1,350 per year --- Util ity loss (gain) One-time --- --- --- Ongoing --- --- --- -$275 per year * Regulation assumed to reduce consumption of existing smokers by 10% and reduces initiation by 10%.
Tables 10-13 illustrate how to use the estimated offsets per person to compute population-level
estimates of health benefits and utility offsets. Because health benefits of quitting smoking or avoiding
initiation depend on smoking history, it is valuable to analyze health benefits and utility offsets for
37
different age cohorts, then aggregate estimated benefits and offsets across cohorts at given points in
time or over time.34 For illustrative purposes, Tables 10-11 analyze how a regulation that reduces
smoking by 10% affects a cohort of 35-year old smokers. Tables 12-13 analyze effects of a regulation
that reduces the smoking initiation rate by 10 percent (2 percentage points) for a cohort of 18-year old
potential initiators.
As shown in Table 10, the total value of offsets for existing 35-year old smokers is derived by
multiplying the number of smokers induced to quit by the regulation by the estimated values for money
savings and utility loss per smoker, and multiplying the number of continuing smokers by the extra
spending per smoker caused by the increase in price. Then for each year the total offset is found by
summing these three components; the total is then discounted to the present using an appropriate
discount rate (3 percent in this example).
The total value of offsets is substantial in the first few years – as much as a billion dollars – due
to utility losses from having 100,000 smokers in the age cohort quit. Withdrawal is costly, and that is
reflected in the table. In the case where utility costs for smokers who quit are primarily withdrawal
costs, the utility losses trail down to $364 million (undiscounted), reflecting costs to continuing smokers
of paying higher prices for cigarettes.
Table 11 shows cumulative changes in health and monetary gains and utility offsets for this
cohort over the first 50 years after the regulation takes effect. In the first decade, utility losses are large
relative to health and monetary benefits. But over time, utility costs taper off while health and monetary
benefits rise, so that the ratio of the two steadily declines. In the 50th year after the regulation takes
effect, utility losses represent 8 percent of health and monetary benefits if all utility loss experienced by
quitters is withdrawal. The share would be higher if some quitters experience ongoing loss but even in
the outside case that all of them do, the loss would not exceed 20 percent of health and monetary gains.
In either case, the offset rate is higher here than in the computations in Table 5, because utility losses
include those of continuing smokers who pay higher prices as well as those who quit.
Tables 12 and 13 provide similar analysis for potential initiators. Here also the total money
savings for dissuaded initiators is smaller than the total utility loss due to higher prices for those who
continue to initiate, resulting in a negative offset of $101 million per year (undiscounted). Because
deterred initiators experience no utility loss, even in the early decades after the regulation comes into
effect, the utility offset ratio is relatively low -- around 12 percent after the 10th year. This rate falls to
7.5 percent by the 50th year and 5 percent by the 80th. The much lower offset ratio among initiators,
compared to existing smokers, underlines that methods for computing utility losses computed from 34 Formally, for a regulation adopted at time 0, for people of a given age j, the expected discounted benefits of the
regulation would be
𝑁𝐵𝑗 = ∑ (1
1 + 𝑑)
𝑡𝑇
𝑡 =1
[ 𝑛𝑗𝑑 ∗ 𝑏𝑗𝑡
𝑑 + 𝑛𝑗𝑐 ∗ 𝑏𝑗𝑡
𝑐 ]
where d is the discount rate (3 percent or 7 percent), 𝑛𝑗𝑑is the number of people of age j who are deterred from
smoking by the regulation (either induced to quit or dissuaded from initiating), 𝑛𝑗𝑐 is the number who continue
smoking or initiating, and 𝑏𝑗𝑡𝑑 and 𝑏𝑗𝑡
𝐼𝑐 are the benefits in the tth year after the regulation takes effect for people in
these two groups respectively. Then the aggregate benefits of the policy can be computed as the sum of the benefits across the age groups.
38
losses of existing smokers only will usually overstate utility offsets to health benefits of regulations.
Given the focus in the TCA on reducing youth initiation, this illustrates the importance of distinguishing
between reductions in smoking due to cessation versus those due to decreased initiation when
estimating utility offsets.
39
Table 10. Example of total offsets from a regulation that increases the effective price of cigarettes: Cohort of 35 -year old existing smokers
Numbers of people affected by the regul ation
(‘000) Mill ions of dollars (undiscounted)
year
Continuing
smokers Smokers who quit
Total utility losses (continuing
smokers and quitters)
if loss of quitters is: Smokers
Newly induced
quitters
Cumulative # of
quitters
Lost util ity
from higher
price per pack
(a) x $400
Money
saving
(c) x $1,500
Lost util ity from quitting
assuming the loss is:
All
withdrawal
(d) x $25,000
25% steady
state*
All
withdrawal
25% steady
state
0 1,012
1 1,002 10 10 -$401 $15 -$253 -$253 -$654 -$654
2 986 15 25 -$395 $38 -$379 -$506 -$774 -$900
3 961 25 51 -$384 $76 -$632 -$885 -$1,017 -$1,270
4 946 15 66 -$378 $99 -$379 -$854 -$758 -$1,232
5 936 10 76 -$374 $114 -$253 -$759 -$627 -$1,133
6 926 10 86 -$370 $129 -$253 -$790 -$623 -$1,161
7 921 5 91 -$368 $137 -$126 -$727 -$495 -$1,095
8 916 5 96 -$366 $144 -$126 -$727 -$493 -$1,093
9 911 5 101 -$364 $152 -$126 -$759 -$491 -$1,123
10 911 0 101 -$364 $152 $0 -$664 -$364 -$1,028
* $25,000 in the first year, $12,500 in the second, and $6,250 thereafter.
Number of 35-year old smokers is from the IPUMs version of the National Health Interview Survey, 2013 (Ruggles et al. 2015). Health benefits do not count gains to continuing smokers from consuming a lower number of packs per year.
40
Table 11. Cumulative benefits and costs of a regulation that increases the effective price of cigarettes: Cohort of 35-year old existing
smokers
Present value in mill ions (3% discount rate)
Util ity offset to health &
monetary benefits
if losses of quitters are:
Util ity losses of continuing
smokers + quitters
if losses of quitters are Health &
monetary
benefits
Total net
benefits Years since the regulation took
effect:
Withdrawal
only
25% steady
state
Withdrawal
only
25% steady
state
10 -$5,589 -$9,262 $9,984 -$4,866 56% 93%
20 -$7,826 -$15,487 $32,188 $8,875 24% 48%
30 -$9,267 -$19,708 $58,182 $29,206 16% 34%
40 -$10,027 -$22,269 $91,522 $59,226 11% 24%
50 -$10,293 -$23,504 $125,982 $92,186 8% 19%
Values are adjusted for mortality risks specific to smokers and nonsmokers. See notes to Table 5 for explanation of data sources and
derivation of health benefits.
41
Table 12. Example of total offsets from a regulation that increases the effective price of cigarettes: Cohort of 18-year old potential
initiators
Numbers of people affected by the
regulation (‘000) Mill ions of dollars (undiscounted)
year Continuing initiators Deterred initiators
Continuing initiators Deterred initiators
Lost util ity from higher price
per pack
(a) x $275
Money savings
(b) x $1,350 Lost util ity
1 808 90 -$222 $121 0
2 808 90 -$222 $121 0
3 808 90 -$222 $121 0
4 808 90 -$222 $121 0
5 808 90 -$222 $121 0
6 808 90 -$222 $121 0
7 808 90 -$222 $121 0
8 808 90 -$222 $121 0
9 808 90 -$222 $121 0
10 808 90 -$222 $121 0
Number of 18-year olds is taken from the IPUMs version of the National Health Interview Survey, 2013 (Ruggles et al. 2015). Health benefits do not count gains to continuing smokers from consuming a lower number of packs per year.
42
Table 13. Cumulative benefits and costs of a regulation that increases the effective price of cigarettes: Cohort of 18-year old potential
initiators
Present value in mill ions (3% discount rate)
Util ity offset to health
& monetary benefits
Util ity losses Health & monetary
benefits
Total net
benefits Continuing
initiators
Deterred
initiators
Years since the regulation took
effect:
10 $1,951 0 $15,895 $13,944 12.3%
20 $3,382 0 $31,092 $27,710 10.9%
30 $4,421 0 $43,768 $39,347 10.1%
40 $5,141 0 $57,128 $51,986 9.0%
50 $5,585 0 $74,323 $68,739 7.5%
60 $5,797 0 $94,796 $88,999 6.1%
70 $5,852 0 $111,876 $106,024 5.2%
80 $5,855 0 $117,846 $111,991 5.0%
Values are adjusted for mortality risks specific to smokers and nonsmokers. See notes to Table 5 for explanation of data sources and derivation of health benefits.
43
5.0 Conclusions
Incorporating preferences inconsistent with standard economic models due to addictive and habitual
goods raises significant analytical challenges for HHS and FDA in meeting the requirements to assess the
costs, benefits, and other impacts of major regulations. The expectation from standard economic theory
that consumers choose to consume only those products where the benefits they get from consumption
outweigh the costs and risks appears not to hold for these goods. However, issues of market failure
associated with addiction and habit do not warrant disregarding altogether the utility losses people may
experience from regulations aimed at curbing consumption of products with health risks. Rather, the
question is how to value these losses when consumers’ preferences, beliefs and/or decision-making
practices differ from the standard model.
Relative to the approaches in the literature, we make several innovations. First, we explicitly
delineate the money savings from reduced consumption of health-harming goods and place those
alongside the health benefits. Second, we show that the utility offsets of reducing consumption have
both short-term withdrawal costs and longer-term steady-state utility costs. Each cost is important in
the short run, but withdrawal costs decline over time. Third, there may be additional costs for people
who continue to consume the addictive or habitual good even after the regulatory intervention, if
regulations change the explicit or implicit price of the good.
Empirically, we measure the utility offsets of regulations using information on consumption of
rational individuals to value utility losses of individuals whose consumption is too high relative to their
preferences. Our estimates of utility losses or withdrawal costs from quitting smoking are meaningful in
the short-run. That said, they are small relative to the lifetime health benefits of quitting: below 5% for
regulations that induce quitting only among smokers who experience no steady-state loss, and no higher
than 20-25% in the outside case that all of them do. Regulations that increase prices for continuing
smokers will lead to somewhat higher offset ratios because of the additional utility losses they induce.
Still, even these higher estimates are well below those reported in previous work. The size of the offset
depends on the discount rate, time frame of the analysis, whether there are ongoing utility losses, and if
the regulation deters initiation or induces quitting. Because most regulations will induce quitting among
smokers who will not miss smoking in the long term, and because people deterred from starting to
smoke have no utility loss, we expect the population-level estimate of the offset ratio will be closer to 5
percent.
We also find that considering initiation separately from cessation is important in considering
consumer surplus changes from policy interventions. For those who are addicted, withdrawal costs are
substantial, while non-initiation avoids these costs.
The current paper focuses on the case of cigarette smoking. That said, the approach developed
here can be extended to analysis of regulations in other areas where there are concerns about whether
observed consumption levels of some segment of consumers may exceed their desired levels. This
situation may characterize a variety of food and tobacco products that have adverse health
consequences when consumed habitually, where issues of addiction, incomplete or non-salient
information, time-inconsistency, inaccurate expectations, and related problems may drive a wedge
between consumers’ decision utility and the experienced utility that will result from their actions.
44
Future research is needed to lower the ranges of uncertainty in the approach we recommend, as
well as to consider other approaches that show promise. These needs are pressing as HHS and its
agencies may well need to conduct regulatory analyses for a range of addictive and habitual goods. HHS
committed in its strategic plan to prevent and reduce tobacco use, and the passage of the TCA in 2009
gives FDA a wide range of new authorities to regulate tobacco. This paper provides a methodology for
evaluating regulations affecting tobacco products and other addictive and habitual goods.
45
References
Anderson, C., D. Burns, K. Dodd, and E. Feuer. 2012. “Birth-Cohort-Specific Estimates of Smoking Behaviors for the U.S. Population.” Risk Analysis 32(Suppl 1): S14-24.
Angeletos, G-M., D. Laibson, A. Repetto, J. Tobacman, and S. Weinberg. 2001. “The Hyperbolic Consumption Model: Calibration, Simulation, and Empirical Evaluation.” Journal of Economic Perspectives 15(3): 47-68.
Arias, E. 2010. “United States Life Tables, 2009.” National Vital Statistics Reports 62(7). Centers for Disease Control and Prevention, U.S. Department of Health and Human Services.
Ashley, E., C. Nardinelli, and R. Lavaty. 2015. “Estimating the Benefits of Public Health Policies that
Reduce Harmful Consumption.” Health Economics 24(5): 617–624.
Australian Productivity Commission. 2010. Gambling: Productivity Commission Inquiry Report.
http://www.pc.gov.au/inquiries/completed/gambling-2009/report/gambling-report-volume1.pdf
Baker, T. B. et al. 2007. “Time to First Cigarette in the Morning as an Index of Ability to Quit Smoking:
Implications for Nicotine Dependence.” Nicotine and Tobacco Research 9(Suppl 4): S555–S570.
Becker, G. and K. Murphy. 1988. "A Theory of Rational Addiction." Journal of Political Economy 96: 675-
700.
Bernheim, B.D. and A. Rangel. 2004. “Addiction and Cue-Triggered Decision Processes.” American
Economic Review 94(5): 1558-1590.
Blackwell, D., J. Lucas, and T. Clarke. 2014. “Summary Health Statistics for U.S. Adults: National Health
Interview Survey, 2012.” National Center for Health Statistics, Vital and Health Statistics 10(260): 1-171.
Borland R., T. Partos, H-H. Yong, M. Cummings, and A. Hyland. 2012. “How Much Unsuccessful Quitting
Activity Is Going on Among Adult Smokers? Data from the International Tobacco Control Four Country
Cohort Survey.” Addiction 107:671-682.
Carpenter, C., and P. Cook. 2008. “Cigarette Taxes and Youth Smoking: New Evidence from National,
State, and Local Youth Risk Behavior Surveys.” Journal of Health Economics 27: 287–299.
Chabris, C., D. Laibson and J. Schuldt. 2008. "Intertemporal Choice." New Palgrave Dictionary of Economics, 2nd edition. Eds. S. Durlauf and L. Blume. New York: Palgrave Macmillan.
Chaloupka, F. 1991. “Rational Addictive Behavior and Cigarette Smoking.” Journal of Political Economy
99(4): 722-742.
46
Chaloupka, F. 2011. FDA Public Comment. Docket: FDA-2010-N-0568.
Chaloupka, F. and K. Warner. 2000. “The Economics of Smoking.” In Handbook of Health Economics,
Vol. IA, eds. A. Culyer and J. Newhouse, 1539-1627. Amsterdam: North Holland/Elsevier.
Chaloupka, F., K. Warner, D. Acemoglu, J. Gruber, F. Laux, W. Max, J. Newhouse, T. Schelling, and J.
Sindelar. 2015. “An Evaluation of the FDA's Analysis of the Costs and Benefits of the Graphic Warning
Label Regulation,” Tobacco Control 24: 112-119.
Chetty, R. 2009. “Sufficient statistics for welfare analysis: a bridge between structural and reduced-form methods.” Annual Review of Economics 1: 451–488. Chetty, R. 2015. “Behavioral Economics and Public Policy: A Pragmatic Perspective.” American Economic
Review Papers and Proceedings 105(5): 1-33.
Choi, S., S. Kariv, W. Müller, and D. Silverman. 2014. "Who Is (More) Rational?" American Economic
Review 104(6): 1518-50.
Cook B., G. Wayne, E. Kafali, Z. Liu, C. Shu, and M. Flores. 2014. “Trends in Smoking among Adults with
Mental Illness and Association between Mental Health Treatment and Smoking Cessation.” Journal of
the American Medical Association 311(2):172-182.
Cutler, D. 2002. “Health Care and the Public Sector.” In Handbook of Public Economics, Vol. 4, eds. A.
Auerbach and M. Feldstein, 2143-2243. Amsterdam: Elsevier/North Holland.
DeCicca, P., D. Kenkel, and A. Mathios. 2002. “Putting Out the Fires: Will Higher Cigarette Taxes Reduce
the Onset of Youth Smoking?” Journal of Political Economy 110: 144–169.
DellaVigna, S. 2009. “Psychology and Economics: Evidence from the Field.” Journal of Economic
Literature 47(2): 315-372.
DellaVigna S. and M.D. Paserman. 2005. “Job Search and Impatience.” Journal of Labor Economics 23(3): 527-588.
Eissenberg, T. and R. Balster. 2000. “Initial Tobacco Use episodes in Children and Adolescents: Current Knowledge, Future Directions.” Drug and Alcohol Dependence 59:41-60. Fiore, M., C. Jaén, T. Baker, et al. 2008. Treating Tobacco Use and Dependence: 2008 Update. Clinical
Practice Guideline. Rockville, MD: U.S. Department of Health and Human Services, Public Health Service.
Flegal, K., B. Graubard, D. Williamson, and M. Gail. 2005. “Excess Deaths Associated With Underweight,
Overweight, and Obesity.” Journal of the American Medical Association 293: 1861–67.
47
Fong, G., D. Hammond, F. Laux, M. Zanna, K. Cummings, R. Borland, and H. Ross. 2004. “The Near-
Universal Experience of Regret among Smokers in Four Countries: Findings from the International
Tobacco Control Policy Evaluation Survey.” Nicotine and Tobacco Research 6 (Suppl 3): S341-S351.
Frederick S., G. Loewenstein, and T. O’Donoghue. 2002. “Time Discounting and Time Preference: A
Critical Review.” Journal of Economic Literature 40: 351-401.
Fujiwara, D. and R. Campbell. 2011. “Valuation Techniques for Social Cost-Benefit Analysis: Stated
Preference, Revealed Preference and Subjective Well-Being Approaches.” Report Prepared for the UK
Treasury and Department for Works and Pensions.
Fujiwara, D. and P. Dolan. Forthcoming. “Happiness-Based Policy Analysis.” In Oxford Handbook of Well-Being and Public Policy, eds. M. Adler and M. Fleurbaey. Oxford, UK: Oxford University Press.
Gallet, C. and J. List. 2003. “Cigarette Demand: A Meta-Analysis of Elasticities.” Health Economics 12(10): 821–835.
Galvan, A, T. Hare, C. Parra, J. Penn, H. Voss, G. Glover, and B. Casey. 2006. “Earlier Development of the Accumbens Relative to Orbitofrontal Cortex Might Underlie Risk-Taking Behavior in Adolescents.” Journal of Neuroscience 26(25): 6885-6892.
Gogtay, N., J. Giedd, L. Lusk, K. Hayashi, D. Greenstein, and A. Vaituzis. 2004. “Dynamic Mapping of Human Cortical Development during Childhood through Early Adulthood.” Procedures of the National Academy of Science 101: 8174-8179
Gruber, J. 2002-2003. “Smoking’s ‘Internalities’.” Regulation 25(4): 52–57.
Gruber, J. and B. Köszegi. 2001. ‘‘Is Addiction ‘Rational’? Theory and Evidence.” Quarterly Journal of
Economics 116(4): 1261-1303.
Gruber, J. and B. Köszegi. 2004. “Tax Incidence When Individuals are Time Inconsistent: The Case of
Cigarette Excise Taxes.” Journal of Public Economics 88(9-10): 1959-1988.
Gruber, J.H., and S. Mullainathan. 2005. “Do Cigarette Taxes Make Smokers Happier?” B.E. Journal of
Economic Analysis & Policy 5(1).
Hanmer, J., W. Lawrence, J. Anderson, R. Kaplan, and D. Fryback. 2006. “Report of Nationally
Representative Values for the Noninstitutionalized US Adult Population for 7 Health-Related Quality-of-
Life Scores.” Medical Decision Making 26(4): 391-400.
Hughes, J.R. 2006. “Clinical Significance of Tobacco Withdrawal.” Nicotine and Tobacco Research 8(2):
153-156.
48
Hughes, J.R. 2007. “Effects of Abstinence from Tobacco: Valid Symptoms and Time Course.” Nicotine and
Tobacco Research 9(3): 315-327.
Hughes, J.R., J. Keely, and S. Naud. 2004. “Shape of the Relapse Curve and Long-term Abstinence among
Untreated Smokers.” Addiction 99(1): 29-38.
Jia, H. and E. Lubetkin. 2010. “Trends in Quality-Adjusted Life-Years Lost Contributed by Smoking and
Obesity.” American Journal of Preventive Medicine 38(2): 138-144.
Kahneman, D. and A. Deaton. 2010. “High Income Improves Evaluation of Life but not Emotional
Well-Being.” Proceedings of the National Academy of Sciences 107(38): 16489-16493.
Kenkel, D. 2000. “Prevention.” In Handbook of Health Economics, Vol. IA, eds. A. Culyer and J.
Newhouse, 1675-1720. Amsterdam: North Holland/Elsevier.
Kenkel, D. and L. Chen. 2000. “A Consumer Information and Tobacco Use.” In Tobacco Control in
Developing Countries, eds. P. Jha and F. Chaloupka, 177-214. Oxford, UK: Oxford University Press.
Laibson, D. 1997. “Golden Eggs and Hyperbolic Discounting.” Quarterly Journal of Economics 112(2):
443-477.
Lasser, K., J. Boyd, S. Woolhandler, D. Himmelstein, D. McCormick, and D. Bor. 2000. “Smoking and
Mental Illness: A Population-Based Prevalence Study.” Journal of the American Medical Association
284(20): 2606-2610.
Mullainathan S., J. Schwartzstein, and W. Congdon. 2012. “A Reduced-Form Approach to Behavioral
Public Finance.” Annual Review of Economics 4: 511-40.
National Research Council. 2013. Subjective Well-Being: Happiness, Suffering, and Other Dimensions of
Experience. Washington, DC: National Academies Press.
Nonnemaker, J. and M. Farrelly. 2011. “Smoking Initiation among Youth: The Role of Cigarette Excise
Taxes and Prices by Race/Ethnicity and Gender.” Journal of Health Economics 30: 560-567.
O'Connor, R., L. Kozlowski, D. Vandenbergh, A. Strasser, M. Grant, and G. Vogler. 2005. “An Examination of Early Smoking Experiences and Smoking Status in a National Cross-sectional Sample.” Addiction 100: 1352–1357. Oncken, C., S. McKee, S. Krishnan-Sarin, S. O’Malley, and C. Mazure. 2005. “Knowledge and Perceived
Risk of Smoking-related Conditions: A Survey of Cigarette Smokers.” Preventive Medicine 40(6): 779–84.
49
Organization for Economic Cooperation and Development. 2013. OECD Guidelines on Measuring
Subjective Well-Being. Paris: OECD Publishing. http://dx.doi.org/10.1787/9789264191655-en.
Orzechowski and Walker. 2012. The Tax Burden of Tobacco. Historical Compilation. Vol. 42.
http://www.taxadmin.org/fta/tobacco/papers/tax_burden_2012.pdf.
Oster, E., E. Dorsey, and I. Shoulson. 2013. “Limited Life Expectancy, Human Capital and Health Investments,” American Economic Review 103(5): 1977-2002. Paterson R., K. Boyle, C. Parmeter, J. Neumann, and P. De Civita. 2008. “Heterogeneity in Preferences for Smoking Cessation.” Health Economics 17(12): 1363-77.
Piasecki, T., M. Piper, and T. Baker. 2010. “Tobacco Dependence: Insights from Investigations of Self-Reported Motives,” Current Directions in Psychological Science 19(6): 395-401.
Piper, M., S. Kenford, M. Fiore, and T. Baker. 2012. “Smoking Cessation and Quality of Life: Changes in
Life Satisfaction Over 3 Years Following a Quit Attempt.” Annals of Behavioral Medicine 43: 262-270.
Reyna, V. and F. Farley. 2006. “Risk and Rationality in Adolescent Decision Making: Implications for Theory, Practice, and Public Policy.” Psychological Science in the Public Interest 7(1): 1-44. Robinson, L. and J. Hammitt. 2013. “Skills of the Trade: Valuing Health Risk Reductions in Benefit -Cost
Analysis.” Journal of Benefit-Cost Analysis 4(1): 107-130.
Robinson, L. and J. Hammitt. 2015. “Valuing Reductions in Fatal Illness Risks: Implications of Recent
Research,” Health Economics. Published online: 30 JUN 2015 (DOI: 10.1002/hec.3214).
Ruggles, S., J. Alexander, K. Genadek, R. Goeken, M. Schroeder, and M. Sobek. 2015. Integrated Public
Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota.
Saad, Linda. 2002. “Tobacco and Smoking.” Gallup Brief, http://www.gallup.com/poll/9910/tobacco-smoking.aspx#risks.
Schauffler, H., S. McMenamin, K. Olson, G. Boyce-Smith, J. Rideout, and J. Kamil. 2001. “Variations in
Treatment Benefits Influence Smoking Cessation: Results of a Randomized Controlled Trial.” Tobacco
Control 10: 175-180.
Shahab, L. and R. West. 2009. “Do Ex-Smokers Report Feeling Happier Following Cessation? Evidence
from a Cross-Sectional Survey.” Nicotine and Tobacco Research 11(5): 553-557.
Shahab, L. and R. West. 2012. “Differences in Happiness between Smokers, Ex-Smokers and Never
Smokers: Cross-Sectional Findings from a National Household Survey. Drug and Alcohol Dependence
121(1): 38-44.
50
Shiffman S., M. Dunbar, X. Li, S. Scholl, H. Tindle, S. Anderson, and S. Ferguson. 2014. “Smoking Patterns
and Stimulus Control in Intermittent and Daily Smokers.” PLoS One 9(3), e89911.
Skiba P. and J. Tobacman. 2008. “Payday Loans, Uncertainty, and Discounting: Explaining Patterns of Borrowing, Repayment, and Default.” Vanderbilt Law and Economics Research Paper No. 08-33. Sloan, F., J. Ostermann, C. Conover, D. Taylor and G. Picone. 2004. The Price of Smoking. Cambridge, MA: MIT Press. Sloan, F. and A. Platt. 2012. “Information, Risk Perceptions, and Smoking Choices of Youth.” Journal of Risk and Uncertainty 42: 161-193.
Sloan, F. and Y. Wang. 2008. “Economic Theory and Evidence on Smoking Behavior of Adults.” Addiction
103: 1777–1785.
Sullivan P., W. Lawrence, and V. Ghushchyan. 2005. “A National Catalog of Preference-Based Scores for
Chronic Conditions in the United States.” Medical Care 43:736-749.
Taylor, D., V. Hasselblad, S. Henley, M. Thun, and F. Sloan. 2002. “Benefits of Smoking Cessation for Longevity.” American Journal of Public Health 92(6): 990-996.
Taylor, G., A. McNeill, A. Girling, A. Farley, N. Lindson-Hawley, and P. Aveyard. 2014. “Changes in Mental
Health after Smoking Cessation: Systematic Review and Meta-Analysis.” British Medical Journal 348: 1-
22.
Tóbiás, Á. 2014. “Optimal Taxation of Addictive Goods Consumed by Naïve Hyperbolic Discounters.”
Mimeo, Yale University.
U.S. Centers for Disease Control and Prevention. 1990. The Health Benefits of Smoking Cessation: A Report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services. http://www.surgeongeneral.gov/library/reports/index.html
U.S. Centers for Disease Control and Prevention. 2000. Reducing Tobacco Use: A Report of the Surgeon
General. Atlanta, GA: U.S. Department of Health and Human Services.
http://www.cdc.gov/tobacco/data_statistics/sgr/2000/complete_report/index.htm
U.S. Centers for Disease Control and Prevention. 2002. Women and Smoking: A Report of the Surgeon
General. Atlanta, GA: U.S. Department of Health and Human Services.
http://www.cdc.gov/tobacco/data_statistics/sgr/2001/complete_report/
51
U.S. Centers for Disease Control and Prevention. 2003. “Prevalence of Current Cigarette Smoking among
Adults and Changes in Prevalence of Current and Someday Smoking: United States, 1996–2001.”
Morbidity and Mortality Weekly Report 289(18): 2355–2356
U.S. Centers for Disease Control and Prevention. 2010. How Tobacco Smoke Causes Disease: The Biology
and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General. Atlanta, GA:
U.S. Department of Health and Human Services. http://www.ncbi.nlm.nih.gov/books/NBK53018/
U.S. Centers for Disease Control and Prevention. 2011. “Quitting Smoking among Adults: United States,
2001-2010.” Morbidity and Mortality Weekly Report 60(44): 1513-1519.
U.S. Centers for Disease Control and Prevention. 2012. Preventing Tobacco Use among Youth and Young Adults: A Report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services. http://www.surgeongeneral.gov/library/reports/preventing-youth-tobacco-use/full-report.pdf U.S. Centers for Disease Control and Prevention. 2014a. The Health Consequences of Smoking: 50 Years
of Progress: A Report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human
Services. http://www.surgeongeneral.gov/library/reports/50-years-of-progress/
U.S. Centers for Disease Control and Prevention. 2014b. “Alcohol-Attributable Deaths and Years of
Potential Life Lost -- 11 States, 2006-2010” Morbidity and Mortality Weekly Report 63(10): 213-216.
U.S. Centers for Disease Control and Prevention. 2014c. “Current Cigarette Smoking Among Adults:
United States, 2005–2013.” Morbidity and Mortality Weekly Report 63(47): 1108-12.
U.S. Food and Drug Administration. 2004. “Final Rule Declaring Dietary Supplements Containing
Ephedrine Alkaloids Adulterated Because They Present an Unreasonable Risk.” Federal Register 69:
6788-6854.
U.S. Food and Drug Administration. 2011. “Required Warnings for Cigarette Packages and
Advertisements, Final Rule.” Federal Register 76: 36628-36777.
U.S. Food and Drug Administration. 2014a. “Analysis of Impacts: Nutrition Facts/Serving Sized Combined
Preliminary Regulatory Impact Analysis”.
http://www.fda.gov/downloads/Food/GuidanceRegulation/GuidanceDocumentsRegulatoryInformation/
LabelingNutrition/UCM385669.pdf.
U.S. Food and Drug Administration. 2014b. “Deeming Tobacco Products to Be Subject to the Federal Food, Drug, and Cosmetic Act, as Amended by the Family Smoking Prevention and Tobacco Control Act (Proposed Rule) – Preliminary Regulatory Impact Analysis.” http://www.fda.gov/downloads/AboutFDA/ReportsManualsForms/Reports/EconomicAnalyses/UCM394933.pdf
52
U.S. Office of Management and Budget. 2003. Circular A-4: Regulatory Analysis.
U.S. Substance Abuse and Mental Health Services Administration. 2010. Results from the 2009 National
Survey on Drug Use and Health: Volume II. Technical Appendices and Selected Prevalence Tables (Office
of Applied Studies, NSDUH Series H-38B, HHS Publication No. SMA 10-4856). Rockville, MD.
U.S. Substance Abuse and Mental Health Services Administration. 2013. Results from the 2012 National
Survey on Drug Use and Health: Detailed Tables.
http://archive.samhsa.gov/data/NSDUH/2012SummNatFindDetTables/DetTabs/NSDUH-
DetTabsTOC2012.htm
Vugrin, E., B. Rostron, S. Verzi, N. Brodsky, T. Brown, C. Choiniere, B. Coleman, A. Paredes, and B.
Apelberg. 2015. “Modeling the Potential Effects of New Tobacco Products and Policies: A Dynamic
Population Model for Multiple Product Use and Harm,” PlosOne, March 27.
Wang, M., X. Wang, T. Lam, K. Viswanath, and S. Chan. 2014. “Ex-Smokers are Happier than Current
Smokers among Chinese Adults in Hong Kong.” Addiction 109: 1165-1171.
Weimer, D., A. Vining, and R. Thomas. 2009. “Cost-Benefit Analysis Involving Addictive Goods:
Contingent Valuation to Estimate Willingness to Pay for Smoking Cessation.” Health Economics 18: 181-
202.
Weinhold, D. and F. Chaloupka. 2014. “Happiness and Smoking.” Unpublished manuscript, London
School of Economics.
Zhou X., J. Nonnemaker, B. Sherrill, A. Gilsenan, F. Coste, and R. West. 2009. “Attempts to Quit Smoking
and Relapse: Factors Associated with Success or Failure from the Attempt Cohort Study.” Addictive
Behaviors 34:365-373.
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Appendix 1: Alternative Approaches
The approach presented above is not the only way that utility offsets to health benefits of regulations
affecting consumption of other addictive or habitual goods could be evaluated. In this section, we
present three other approaches that have been suggested in the literature. They are not specifically
concerned with measuring utility offsets to health benefits, but rather offer ways of measuring benefits
of proposed regulations more generally. Each is promising, but all have significant theoretical and
empirical issues that need to be addressed before they can be implemented.
A.1 Measuring Willingness to Pay
In many contexts, economists use willingness to pay estimates to infer the value of changes in
consumption. In many contexts, economists use willingness to pay estimates to infer the value of
changes in consumption. In this section, we consider how data on purchases of cessation products can
be used to estimate this willingness to pay. As we discuss, the results raise many questions that cannot
be resolved without more research. Thus the approach described in the prior section appears preferable
at this time.
Consider a current smoker contemplating quitting. Denote the self-assessed value of quitting
smoking as V. This is what we wish to measure. There are products that people can buy that increase
the probability of successfully quitting, including counseling, nicotine replacement products (patches,
lozenges, gum) and prescription medications like Chantix and Zyban. Assume that the probability of
successfully quitting smoking is dn without aids and dw with aids, so that ∆d=(dw-dn) is the impact of
using aids on the probability of cessation. Empirically, the incremental effectiveness of smoking aids is
about 5 percent to 20 percent,35 though we consider what smokers believe below. Finally, suppose that
the monetary cost of these aids is p and the withdrawal cost of quitting smoking is the same with or
without aids. The person will purchase the aid provided the additional benefit to using the product is
greater than the monetary cost: ∆d∙V > p. If we know the price at which people will buy cessation aids,
p, and how consumers perceive the effectiveness of aids, ∆d, we can use this equation to calculate V: V
= p/∆d.
Willingness to pay for cessation aids can be estimated using either revealed- or stated-
preference methods (e.g., Paterson et al. 2008, Weimer et al. 2009).36 Paterson et al. (2008, Table V)
estimate that heavy smokers who wish to quit would be willing to pay about $145 (Canadian $2004) for
a nicotine patch that would increase their probability of successfully quitting from 1.5 percent to 6
percent. This implies a typical value of quitting of $145/4.5% = $3,200 (Canadian). Schauffler et al.
(2001) report that the fraction of smokers in the treatment group of a randomized controlled trial who
used nicotine gum or patches increased from 14 percent to 23 percent when the cost of these products
to them was reduced from the market price to zero (through health-insurance coverage). Assuming the
35 Fiore et al. (2008), Table 6.26, p. 109. 36 Revealed-preference methods use actual purchase behavior. Stated-preference methods rely on smokers’ responses to survey questions asking if they would take a specific action to increase their chance of quitting.
54
cost to control-group subjects of these products is $25037 and assuming a linear demand curve implies
that the average WTP among the 23 percent who would use the products if they were free is $320.38
Schauffler et al. report that 13 percent of subjects in their control group and 18 percent in the treatment
group quit smoking, implying an average incremental effectiveness of 5 percent. Taking this as an
estimate of perceived effectiveness and combining it with average WTP yields a typical value of quitting
of $320/5% = $6,400. Volpp et al. (2009) consider the impact of financial incentives to stop smoking.
The authors conduct a randomized control trial where participants in the treatment group received up
to $750 to quit smoking. They found that rates of prolonged abstinence from smoking after 9 or 12
months of follow-up were 9.7 percent higher in the group that was eligible for financial incentives than
in the control group. Dividing the total incentive payments by the difference in successful abstinence
yields an estimate of $750/9.7% = $7,700. The illustrative values of cessation thus range from about
$3,200 to $7,700.
There are two major difficulties with relying on data on cessation products that FDA will have to
address before it can be implemented. First, one needs to decide what to make of the 77 percent of
smokers who decline to use the cessation aids when free. In practice, this group includes both smokers
who do not wish to quit and smokers who wish to quit but do not purchase cessation aids. About two-
thirds of smokers say they wish to quit. We suspect that the non-takers are more similar to those who
take up the cessation aids than to those who do not wish to quit. As a result, it is reasonable to
attribute the value of cessation estimated for smokers who do purchase cessation aids to smokers who
wish to quit but do not purchase such aids (e.g., $6,400).
The second problem is that these values seem far too low to be plausible. Indeed, the implied
value of cessation is far below what most smokers spend on cigarettes in a year. It is not entirely clear
what explains this discrepancy. It may be that such people perceive ∆d as smaller or the full price p as
larger than used above. Alternatively, they may incorrectly believe they will quit in a future period at
lower cost. Before using this method, FDA would need to obtain survey evidence of existing smokers to
measure their perceived ∆d and p.
Willingness to pay estimates can be extended to non-initiators as well. For a non-smoker, the
benefit of not initiating is the expected value of a lottery that reflects uncertainty about whether or not
the smoker will wish to quit at some time after initiation.39 If he smokes and wishes to quit, the harm
from having initiated is the withdrawal costs of cessation.
In principle, withdrawal costs can be estimated using the willingness to pay approach, by
considering an aid that virtually eliminated these costs and comparing that to an aid that does not.
Weimer et al. (2009) elicited smokers’ willingness to pay for a hypothetical drug “that would completely
block your craving for cigarettes.” The drug would be administered by injection; it would work for one 37 Schauffler et al. report the cost of the treatment as $32,487 for the 124 subjects who used gum or patches and 6 who used behavioral therapy. Assuming the 6 are not included in the 124 implies an average cost of $250 (≈$32,487/130) per subject who used the cessation products. 38 The estimated inverse demand is p = 640 – 28 q where p is WTP ($) and q is the percentage purchasing the cessation product; mean WTP = $640/2. 39 In this section, “wish to quit” means the smoker would quit if there were no physical, psychic, or financial costs of quitting. Given that there are large physical and psychic costs, many smokers who “wish to quit” may choose not to attempt to quit.
55
year and could be renewed with another injection. Estimated mean willingness to pay for this drug is
roughly $300 - $350 (depending on model specification). As these values are smaller than the estimates
of willingness to pay that do not eliminate the physical and psychic costs, they are not plausible
estimates of the value of quitting if there were no physical and psychic costs. An alternative is to
estimate withdrawal costs by matching withdrawal symptoms to quality-of-life disutilities in the
literature, as was done above.
The cost of non-initiation is the harm from preventing initiation for those smokers who never
regret initiation. As we argue below, very few rational individuals initiate smoking. Thus, we suspect
that these costs are small.
A.2 Calculating Compensating Differentials
A second approach to valuing the costs of smoking changes is to evaluate the benefits of regulation with
an assumed utility function.40 In equation (1), W is consumer welfare without a regulatory intervention;
denote this Wwithout. Welfare with a regulatory intervention is denoted Wwith. The welfare benefit of a
regulation to consumers is given by C, where Wwith(Y-C) = Wwithout(Y). The goal is to calculate C for different
policies.
Gruber and Köszegi (2001) make a start on this for the utility function in equation (1), estimating
the optimal tax needed to counter time inconsistency. They limited their analysis to ‘sophisticated’
hyperbolic discounters – those who are aware of their time inconsistency. Sparked by this panel, the
solution to this problem has been solved for ‘naïve’ hyperbolic discounters – those who do not recognize
their time inconsistency – by Áron Tóbiás of Yale University (Tóbiás 2014), who worked under James
Choi. Both the Gruber-Köszegi solution and the Tóbiás solution are derived for a steady state – a person
who will smoke the same amount over time. That is relevant for existing smokers but clearly not
relevant for potential initiators. Thus, this approach is only relevant for the cessation margin.
In practice, the calculations that result from this are extremely sensitive to the parameterization
of the time inconsistency problem that consumers face. To see this, note that the optimal tax to correct
time inconsistency is given by
Optimal taxi ≡ τi = θi∙(1- β)∙HS ,
where HS the discounted lifetime value of the health costs of smoking41 and θi is a parameter that takes
on two values, one for naïve hyperbolic consumers and a second for sophisticated hyperbolic
consumers.
Figure A-1 shows the optimal corrective tax as a function of β using parameters similar to those
chosen in Gruber and Köszegi (2001).42 In this example, the present value of the health harm from
40 Chetty (2009) and Mullainathan, Schwarstzstein and Congdon (2012) review the theory and practice of reduced-form versus structural approaches to standard and behavioral welfare economics. 41 Formally, HS =
δ(1−d)
1−δ(1−d) hS, where δ=1/(1+ρ), ρ is the discount rate for a non-hyperbolic consumer, and d is the
rate at which stock of past consumption declines over time (e.g., how rapidly the harmful stock of past cigarette consumption changes). 42 Specifically, we assume d=0.8, λ=0.7, and δ=1.
56
cigarettes is assumed to be $30.45 per pack. At β=0, none of this cost is internalized, and so the optimal
corrective taxation is that amount. At β=1, the full cost is internalized, and so the optimal corrective tax
is zero.
A general consensus is that the average β in the population is likely about 0.7 (Angeletos et al.
2001), in which case the optimal corrective tax is about $8 per pack. However, there is almost certainly
heterogeneity in β, and this will be important. For example, studies show that current smokers, problem
gamblers, and children have lower β’s (Chabris et al. 2008). Additionally, given the widespread
knowledge about the harms of smoking and the dramatic reduction in smoking over time, one expects
that shortsighted people are overly represented in the smoking population. Added to this is the fact that
adults with mental illness are more likely to be smokers than the rest of the population. Estimates of β
in the literature range from as low as 0.4 (DellaVigna and Paserman 2005) to as high as 0.9 (Skiba and
Tobacman 2008). Further research on discounting among current smokers would be valuable.
A.3 Directly Measuring Effects of Smoking Cessation on Subjective Well Being
Information on subjective well-being (or “happiness”) is an additional way to measure the impact of
regulations. If one interprets measures of self-reported happiness as experienced utility, this method
can indicate how regulations affect the utility of those they influence.
Measures of subjective well-being are derived using surveys to ask questions about “how
happy” or “how satisfied” respondents are with their lives. Answers can then be translated into
monetary values by estimating the change in income that provides the same change in subjective well-
being as the event of interest.
$0
$5
$10
$15
$20
$25
$30
$35
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
β
Figure A-1: Variation in Optimal Corrective Taxation with β
57
An increasing number of studies have looked at the relationship between smoking and
measures of subjective well-being.43 It is clear that many smokers are unhappy with their smoking, and
former smokers are happy with their decision to quit (Shahab and West 2009, Kahneman and Deaton
2010, Piper et al. 2012, Taylor et al. 2014, Wang et al. 2014, Weinhold and Chaloupka 2014). In a cost-
benefit context, it is a question whether regulatory-induced changes in smoking have the same effect on
well-being as people who quit for other reasons. Gruber and Mullainathan (2005) find that higher
cigarette excise taxes are associated with greater subjective well-being among those with a propensity
to smoke in the U.S. and Canada. More systematic review is needed to evaluate existing research and its
applicability to the specific types of policies HHS is likely to pursue, and more work is needed to
determine the relationship of empirical measures of subjective well-being to monetary values.
43 A useful survey is Fujiwara and Dolan (forthcoming). The field increasingly emphasizes development of best practices for use in policy analysis and other settings, including recent U.S., U.K., and OECD recommendations (National Research Council 2013, Fujiwara and Campbell 2011, Organization for Economic Cooperation and Development 2013). The U.S. recommendations developed by the National Research Council focus primarily on measuring subjective well-being, while the U.K. and OECD guidance also address the use of these measures in benefit-cost analysis.